Emerging trends in federated learning: from model fusion to federated X learning

Federated learning is a new learning paradigm that decouples data collection and model training via multi-party computation and model aggregation. As a flexible learning setting, federated learning has the potential to integrate with other learning frameworks. We conduct a focused survey of federate...

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Published in	International journal of machine learning and cybernetics Vol. 15; no. 9; pp. 3769 - 3790
Main Authors	Ji, Shaoxiong, Tan, Yue, Saravirta, Teemu, Yang, Zhiqin, Liu, Yixin, Vasankari, Lauri, Pan, Shirui, Long, Guodong, Walid, Anwar
Format	Journal Article
Language	English
Published	Berlin/Heidelberg Springer Berlin Heidelberg 01.09.2024 Springer Nature B.V
Subjects	Adaptive algorithms Algorithms Artificial Intelligence Bayesian analysis Communication Complex Systems Computational Intelligence Control Engineering Federated learning Informatics Machine learning Mechatronics Original Article Paradigms Pattern Recognition Privacy Regularization Regularization methods Robotics State-of-the-art reviews Systems Biology Taxonomy Trends Unsupervised learning Model fusion Learning algorithms Federated learning
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ISSN	1868-8071 1868-808X
DOI	10.1007/s13042-024-02119-1

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Abstract	Federated learning is a new learning paradigm that decouples data collection and model training via multi-party computation and model aggregation. As a flexible learning setting, federated learning has the potential to integrate with other learning frameworks. We conduct a focused survey of federated learning in conjunction with other learning algorithms. Specifically, we explore various learning algorithms to improve the vanilla federated averaging algorithm and review model fusion methods such as adaptive aggregation, regularization, clustered methods, and Bayesian methods. Following the emerging trends, we also discuss federated learning in the intersection with other learning paradigms, termed federated X learning, where X includes multitask learning, meta-learning, transfer learning, unsupervised learning, and reinforcement learning. In addition to reviewing state-of-the-art studies, this paper also identifies key challenges and applications in this field, while also highlighting promising future directions.
AbstractList	Federated learning is a new learning paradigm that decouples data collection and model training via multi-party computation and model aggregation. As a flexible learning setting, federated learning has the potential to integrate with other learning frameworks. We conduct a focused survey of federated learning in conjunction with other learning algorithms. Specifically, we explore various learning algorithms to improve the vanilla federated averaging algorithm and review model fusion methods such as adaptive aggregation, regularization, clustered methods, and Bayesian methods. Following the emerging trends, we also discuss federated learning in the intersection with other learning paradigms, termed federated X learning, where X includes multitask learning, meta-learning, transfer learning, unsupervised learning, and reinforcement learning. In addition to reviewing state-of-the-art studies, this paper also identifies key challenges and applications in this field, while also highlighting promising future directions.
Author	Tan, Yue Walid, Anwar Saravirta, Teemu Long, Guodong Ji, Shaoxiong Vasankari, Lauri Pan, Shirui Yang, Zhiqin Liu, Yixin
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Cites_doi	10.3390/s20144048 10.1109/MIS.2020.3014880 10.1145/3459637.3482252 10.1109/TIFS.2023.3255171 10.1109/MIS.2020.2988525 10.1109/INFOCOM41043.2020.9155494 10.1609/aaai.v37i9.26331 10.1007/978-3-030-58951-6_24 10.1145/3501816 10.1609/aaai.v35i9.16920 10.1145/3298981 10.1145/3580305.3599346 10.1609/aaai.v35i10.17118 10.24963/ijcai.2022/303 10.1109/JIOT.2023.3324666 10.1109/TCAD.2022.3205551 10.1109/MIS.2020.2994942 10.1609/aaai.v36i8.20825 10.1007/s11280-022-01046-x 10.1109/TMI.2023.3235757 10.1109/CVPR46437.2021.01549 10.1109/MCOM.001.1900461 10.1109/MIS.2020.2988604 10.1109/MSP.2020.2975749 10.1109/TNNLS.2022.3224252 10.1145/3534678.3539384 10.1145/3581783.3612217 10.1016/j.apenergy.2022.120526 10.1109/TNNLS.2020.3015958 10.1016/j.comnet.2021.108468 10.1109/GCWkshps56602.2022.10008598 10.1109/CVPR42600.2020.00975 10.1109/CVPR52688.2022.00988 10.1007/978-3-030-60548-3_15 10.1016/j.neucom.2023.126831 10.36227/techrxiv.12503420 10.1109/TITS.2023.3303991 10.1109/TNNLS.2019.2953131 10.1155/2022/2913293 10.1609/aaai.v38i13.29329 10.1109/TETC.2020.2983404 10.1109/JIOT.2023.3302065 10.1109/CVPR46437.2021.01057 10.1109/JIOT.2023.3302792 10.24963/ijcai.2021/720 10.24963/ijcai.2021/720 10.1109/INFOCOM41043.2020.9155268 10.1007/978-3-031-16437-8_19 10.1145/3539597.3570463 10.1007/978-3-031-20056-4_40 10.1007/s11280-019-00775-w 10.1145/3397271.3401081 10.1109/TKDE.2022.3220219 10.1109/CVPR52688.2022.00991 10.1109/CVPR52688.2022.00993 10.1109/TNSE.2020.2996612 10.1145/3394486.3403176 10.1016/j.knosys.2023.110347 10.1109/TPDS.2023.3289444 10.24963/ijcai.2022/306 10.24963/ijcai.2021/217 10.1109/TPDS.2022.3230938 10.1609/aaai.v35i9.16960 10.1109/CVPR52729.2023.01563 10.1007/978-3-030-60636-7_1 10.1007/978-3-030-63076-8_17 10.1109/IJCNN55064.2022.9892624 10.1016/j.knosys.2022.109384 10.1145/3534678.3539308 10.1017/9781108571401 10.1109/TKDE.2022.3172903 10.1016/j.media.2023.102976 10.1109/TII.2021.3113708 10.1109/IJCNN48605.2020.9207469 10.1109/ICCV51070.2023.01509 10.24963/ijcai.2021/202 10.1109/IJCNN.2019.8852464 10.1007/978-3-030-72188-6_6 10.1609/aaai.v35i8.16849 10.1145/3604939 10.1631/FITEE.2200268 10.1109/JSAC.2020.3036946 10.1145/3510031 10.1109/MASS52906.2021.00031 10.1609/aaai.v36i8.20903 10.1109/ICCV51070.2023.01559 10.1109/TNNLS.2022.3233093 10.1145/3523227.3546771 10.1007/978-3-030-63076-8_1 10.1109/COMST.2020.2986024 10.1007/978-3-030-63076-8_14 10.1109/GLOBECOM48099.2022.10000892 10.1007/s40747-020-00247-z 10.1609/aaai.v35i12.17291 10.1109/ISPDC52870.2021.9521631 10.1609/aaai.v36i8.20819 10.1109/TITS.2023.3286439 10.1609/aaai.v35i10.17053 10.1109/JIOT.2022.3201231
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References	ChenZLiWXingXYuanYMedical federated learning with joint graph purification for noisy label learningMed Image Anal202390102976 Zhan Y, Zhang J (2020) An incentive mechanism design for efficient edge learning by deep reinforcement learning approach. In: IEEE international conference on computer communications, IEEE, pp 2489–2498 QiTWuFWuCLyuLXuTLiaoHYangZHuangYXieXFairvfl: a fair vertical federated learning framework with contrastive adversarial learningAdv Neural Inf Process Syst20223578527865 ChenYSunXJinYCommunication-efficient federated deep learning with layerwise asynchronous model update and temporally weighted aggregationIEEE Trans Neural Netw Learn Syst2020311042294238 Xu J, Wang F (2019) Federated learning for healthcare informatics. arXiv preprint arXiv:1911.06270 ThapaCMahawaga ArachchigePCCamtepeSSunLSplitfed: when federated learning meets split learningProc AAAI Conf Artif Intell20223688485849310.1609/aaai.v36i8.20825 Reddi S, Charles Z, Zaheer M, Garrett Z, Rush K, Konečnỳ J, Kumar S, McMahan HB (2021) Adaptive federated optimization. In: International conference on learning representations Ji S, Long G, Pan S, Zhu T, Jiang J, Wang S, Li X(2019) Knowledge transferring via model aggregation for online social care. arXiv preprint arXiv:1905.07665 Tang Z, Wang Y, He X, Zhang L, Pan X, Wang Q, Zeng R, Zhao K, Shi S, He B, et al (2023) Fusionai: decentralized training and deploying llms with massive consumer-level gpus. arXiv preprint arXiv:2309.01172 McMahan HB, Moore E, Ramage D, Hampson S, et al (2017) Communication-efficient learning of deep networks from decentralized data. In: International Conference on artificial intelligence and statistics, pp 1273–1282 KhalatbarisoltaniABoulonLHuXIntegrating model predictive control with federated reinforcement learning for decentralized energy management of fuel cell vehiclesIEEE Trans Intell Transp Syst202310.1109/TITS.2023.3303991 Park S, Han S, Wu F, Kim S, Zhu B, Xie X, Cha M ( 2023) Feddefender: client-side attack-tolerant federated learning. In: Proceedings of the 29th ACM SIGKDD conference on knowledge discovery and data mining, pp 1850–1861 SinghSPJaggiMModel fusion via optimal transportAdv Neural Inf Process Syst2020332204522055 TanYLiuYLongGJiangJLuQZhangCFederated learning on non-iid graphs via structural knowledge sharingProc AAAI Conf Artif Intel20233799539961 WangBYuanZYingYYangTMemory-based optimization methods for model-agnostic meta-learning and personalized federated learningJ Mach Learn Res2023241464596092 Li Q, Wen Z, He B (2019) Federated learning systems: vision, hype and reality for data privacy and protection. arXiv preprint arXiv:1907.09693 Ji S, Jiang W, Walid A, Li X (2020) Dynamic sampling and selective masking for communication-efficient federated learning. arXiv preprint arXiv:2003.09603 He C, Tan C, Tang H, Qiu S, Liu J (2019) Central server free federated learning over single-sided trust social networks. arXiv preprint arXiv:1910.04956 WatkinsCJDayanPQ-learningMach Learn19928279292 Mansour Y, Mohri M, Ro J, Suresh AT (2020) Three approaches for personalization with applications to federated learning. arXiv preprint arXiv:2002.10619 ChaHParkJKimHBennisMKimS-LProxy experience replay: federated distillation for distributed reinforcement learningIEEE Intell Syst202035494101 LiCWangHCommunication efficient federated learning for generalized linear banditsAdv Neural Inf Process Syst2022353841138423 Wen Y, Geiping JA, Fowl L, Goldblum M, Goldstein T (2022) In: Chaudhuri K, Jegelka S, Song L, Szepesvari C, Niu G, Sabato S (eds) Proceedings of the 39th international conference on machine learning. Proceedings of machine learning research. Fishing for user data in large-batch federated learning via gradient magnification, vol 162, pp 23668–23684. https://proceedings.mlr.press/v162/wen22a.html SattlerFMüllerK-RSamekWClustered federated learning: model-agnostic distributed multitask optimization under privacy constraintsIEEE Trans Neural Netw Learn Syst2020328371037224296542 Li X, Huang K, Yang W, Wang S, Zhang Z (2020) On the convergence of Fedavg on non-iid data. In: International conference on learning representations HijaziNMAloqailyMGuizaniMOuniBKarrayFSecure federated learning with fully homomorphic encryption for IoT communicationsIEEE Internet of Things J202310.1109/JIOT.2023.3302065 EzzeldinY.HYanSHeCFerraraEAvestimehrA. SFairfed: Enabling group fairness in federated learningProc AAAI Conf Artif Intell20233774947502 ZhangYZhangWPuLLinTYanJTo distill or not to distill: towards fast, accurate and communication efficient federated distillation learningIEEE Internet of Things J202310.1109/JIOT.2023.3324666 YangYYangRPengHLiYLiTLiaoYZhouPFedACK: federated adversarial contrastive knowledge distillation for cross-lingual and cross-model social bot detectionProc ACM Web Conf2023202313141323 GuptaSHuangYZhongZGaoTLiKChenDRecovering private text in federated learning of language modelsAdv Neural Inf Process Syst20223581308143 ZhuHZhangHJinYFrom federated learning to federated neural architecture search: a surveyComplex Intell Syst202072639657 Dennis DK, Li T, Smith V (2021) Heterogeneity for the win: one-shot federated clustering. In: International conference on machine learning, PMLR, pp 2611–2620 Fan C, Liu P (2020) Federated generative adversarial learning. arXiv preprint arXiv:2005.03793 Durmus AE, Yue Z, Ramon M, Matthew M, Paul W, Venkatesh S (2021) Federated learning based on dynamic regularization. In: International conference on learning representations Xiao J, Du C, Duan Z, Guo W (2021) A novel server-side aggregation strategy for federated learning in non-iid situations. In: 2021 20th International symposium on parallel and distributed computing (ISPDC), IEEE, pp 17–24 Bietti A, Wei C-Y, Dudik M, Langford J, Wu S (2022) Personalization improves privacy-accuracy tradeoffs in federated learning. In: Chaudhuri K, Jegelka S, Song L, Szepesvari C, Niu G, Sabato S (eds) Proceedings of the 39th international conference on machine learning. Proceedings of machine learning research, vol 162, pp 1945–1962. https://proceedings.mlr.press/v162/bietti22a.html QiuDXueJZhangTWangJSunMFederated reinforcement learning for smart building joint peer-to-peer energy and carbon allowance tradingAppl Energy2023333120526 Han S, Park S, Wu F, Kim S, Wu C, Xie X, Cha M( 2022) Fedx: unsupervised federated learning with cross knowledge distillation. In: European conference on computer vision, Springer, pp 691–707 FengSLiBYuHLiuYYangQSemi-supervised federated heterogeneous transfer learningKnowl-Based Syst2022252109384 Smith V, Chiang C-K, Sanjabi M, Talwalkar AS (2017) Federated multi-task learning. In: Advances in neural information processing systems, pp 4427– 4437 Yu FX, Rawat AS, Menon AK, Kumar S (2020) Federated learning with only positive labels. In: International conference on machine learning JinXBuJYuZZhangHWangYFedCrack: federated transfer learning with unsupervised representation for crack detectionIEEE Trans Intell Transp Syst202310.1109/TITS.2023.3286439 Lu Y, Chen L, Zhang Y, Zhang Y, Han B, Cheung Y-m, Wang H (2023) Federated learning with extremely noisy clients via negative distillation. arXiv preprint arXiv:2312.12703 Karimireddy SP, Jaggi M, Kale S, Mohri M, Reddi SJ, Stich SU, Suresh AT (2020) Mime: Mimicking centralized stochastic algorithms in federated learning. arXiv preprint arXiv:2008.03606 Zhang X, Li Y, Li W, Guo K, Shao Y (2022) Personalized federated learning via variational bayesian inference. In: International conference on machine learning, PMLR, pp 26293–26310 Huang Y, Gupta S, Song Z, Li K, Arora S (2021) Evaluating gradient inversion attacks and defenses in federated learning. In: Ranzato M, Beygelzimer A, Dauphin Y, Liang PS, Vaughan JW (eds) Advances in neural information processing systems, vol 34, pp 7232–7241. https://proceedings.neurips.cc/paper/2021/file/3b3fff6463464959dcd1b68d0320f781-Paper.pdf Hoang M, Hoang N, Low BKH, Kingsford C ( 2019) Collective model fusion for multiple black-box experts. In: International conference on machine learning, PMLR, pp 2742–2750 Jiang J, Ji S, Long G (2020) Decentralized knowledge acquisition for mobile internet applications. World Wide Web Tan AZ, Yu H, Cui L, Yang Q (2021) Towards personalized federated learning. arXiv preprint arXiv:2103.00710 Shang X, Huang G, Lu Y, Lou J, Han B, Cheung Y-m, Wang H (2023) Federated semi-supervised learning with annotation heterogeneity. arXiv preprint arXiv:2303.02445 Zhu L, Lin H, Lu Y, Lin Y, Han S (2021) Delayed gradient averaging: tolerate the communication latency for federated learning. In: Ranzato M, Beygelzimer A, Dauphin Y, Liang PS, Vaughan JW (eds) Advances in neural information processing systems, vol 34, pp 29995–30007. https://proceedings.neurips.cc/paper/2021/file/fc03d48253286a798f5116ec00e99b2b-Paper.pdf Briggs C, Fan Z, Andras P (2020) Federated learning with hierarchical clustering of local updates to improve training on non-iid data. In: International joint conference on neural network Liang X, Lin Y, Fu H, Zhu L, Li, X ( 2022) Rscfed: random sampling consensus federated semi-supervised learning. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 10154–10163 ChenCLiuYMaXLyuLCalfat: calibrated federated adversarial training with label skewnessAdv Neural Inf Process Syst20223535693581 Wang H-P, Stich S, He Y, Fritz M (2022) Progfed: effective, communication, and computation efficient federated learning by progressive training. In: International conference on machine learning, PMLR, pp 23034–23054 He C, Li S, So J, Zhang M, Wang H, Wang X, Vepakomma P, Singh A, Qiu H, Shen L, Zhao P, Kang Y, Liu Y, Raskar R, Yang Q, Annavaram M, Avestimehr S (2020) FedML: a research library and benchmark for federated machine learning. arXiv preprint arXiv:2007.13518 Grammenos A, Mendoza Smith R, Crowcroft J, Mascolo C (2020) Federated principal component analysis. In: Advances in neural information processing systems Zhuang W, Wen Y, Zhang S (2022) Diverge 2119_CR76 2119_CR75 S Gupta (2119_CR164) 2022; 35 2119_CR74 B Zhao (2119_CR160) 2022; 36 2119_CR71 Q Yang (2119_CR107) 2019; 10 Q Guo (2119_CR23) 2023; 560 2119_CR78 H Gao (2119_CR181) 2021; 35 2119_CR80 RS Sutton (2119_CR137) 1988; 3 Z Chen (2119_CR203) 2023; 90 Y.H Ezzeldin (2119_CR45) 2023; 37 2119_CR66 2119_CR65 D Chai (2119_CR120) 2020; 36 2119_CR63 2119_CR62 X Wang (2119_CR22) 2020; 39 2119_CR60 C He (2119_CR67) 2020; 33 K Sohn (2119_CR129) 2020; 33 J Hong (2119_CR91) 2023; 37 X-X Wei (2119_CR79) 2023 2119_CR68 J Liu (2119_CR18) 2021; 199 MS Ozdayi (2119_CR159) 2021; 35 E Diao (2119_CR77) 2022; 35 X Cao (2119_CR162) 2021; 35 CJ Watkins (2119_CR136) 1992; 8 Y Tan (2119_CR194) 2023; 37 R Liu (2119_CR156) 2021; 35 2119_CR99 F Zhang (2119_CR94) 2023; 24 2119_CR98 2119_CR97 2119_CR96 M Alawad (2119_CR122) 2020; 9 2119_CR95 2119_CR182 2119_CR93 2119_CR184 2119_CR92 2119_CR186 WYB Lim (2119_CR112) 2020; 22 2119_CR187 C Chen (2119_CR90) 2022; 35 2119_CR188 S Che (2119_CR201) 2022; 13 2119_CR189 2119_CR88 2119_CR86 2119_CR85 C He (2119_CR61) 2022; 36 2119_CR84 2119_CR193 Z Zhu (2119_CR185) 2022; 162 2119_CR82 2119_CR195 2119_CR81 2119_CR197 2119_CR199 2119_CR89 T Qi (2119_CR87) 2022; 35 Y Yang (2119_CR70) 2023; 2023 2119_CR161 2119_CR163 2119_CR165 Y Chen (2119_CR52) 2020; 35 2119_CR166 2119_CR167 2119_CR168 2119_CR169 S Feng (2119_CR53) 2022; 252 Y Tan (2119_CR33) 2022; 36 2119_CR171 2119_CR172 2119_CR173 2119_CR174 2119_CR175 H Zhu (2119_CR152) 2020; 7 2119_CR176 2119_CR177 Y Yang (2119_CR149) 2021; 35 J-B Grill (2119_CR133) 2020; 33 2119_CR141 2119_CR142 2119_CR143 G Long (2119_CR35) 2023; 26 2119_CR144 2119_CR145 2119_CR146 2119_CR147 2119_CR148 Y Cui (2119_CR192) 2022 Y Chang (2119_CR178) 2023; 18 T Lin (2119_CR69) 2020; 33 2119_CR150 2119_CR151 2119_CR8 2119_CR153 2119_CR154 2119_CR155 2119_CR9 Y Kang (2119_CR83) 2022; 13 2119_CR1 2119_CR4 2119_CR3 2119_CR6 SP Singh (2119_CR119) 2020; 33 2119_CR5 Y Chen (2119_CR10) 2020; 31 X Xu (2119_CR200) 2021; 18 2119_CR121 A Khalatbarisoltani (2119_CR139) 2023 2119_CR123 2119_CR124 2119_CR125 2119_CR126 2119_CR127 2119_CR128 Z Liu (2119_CR198) 2022; 13 Z Sun (2119_CR191) 2022; 35 H Cha (2119_CR101) 2020; 35 B Wang (2119_CR64) 2023; 24 M Zhu (2119_CR204) 2023 P Xiao (2119_CR43) 2023; 37 2119_CR131 2119_CR132 NM Hijazi (2119_CR179) 2023 2119_CR134 2119_CR135 H Yang (2119_CR50) 2020; 8 2119_CR138 X Yang (2119_CR130) 2022; 35 X Jin (2119_CR54) 2023 P Zhao (2119_CR180) 2022; 10 2119_CR11 2119_CR19 S Niknam (2119_CR113) 2020; 58 2119_CR17 2119_CR100 2119_CR16 2119_CR15 2119_CR102 2119_CR14 2119_CR103 2119_CR13 2119_CR104 T Li (2119_CR12) 2020; 37 W Mai (2119_CR105) 2023; 18 2119_CR106 2119_CR108 2119_CR109 C Thapa (2119_CR157) 2022; 36 Y Tan (2119_CR7) 2022; 35 X Wu (2119_CR20) 2020; 20 S Zawad (2119_CR158) 2021; 35 2119_CR110 2119_CR111 2119_CR114 2119_CR115 2119_CR116 2119_CR117 2119_CR118 Z Liu (2119_CR202) 2022; 36 CT Dinh (2119_CR32) 2022 2119_CR31 2119_CR30 2119_CR39 2119_CR38 2119_CR37 2119_CR36 2119_CR34 2119_CR205 2119_CR206 2119_CR207 2119_CR208 H Wang (2119_CR2) 2020; 33 Z Wu (2119_CR72) 2023 X Cao (2119_CR58) 2023; 265 K Fan (2119_CR170) 2023 2119_CR21 2119_CR29 2119_CR28 2119_CR27 2119_CR26 2119_CR25 2119_CR24 K Wang (2119_CR51) 2022 2119_CR55 O Marfoq (2119_CR59) 2021; 34 D Qiu (2119_CR140) 2023; 333 2119_CR56 F Liang (2119_CR196) 2021; 35 Y Liu (2119_CR48) 2020; 35 2119_CR44 2119_CR42 2119_CR41 2119_CR40 Y Zhang (2119_CR73) 2023 F Sattler (2119_CR57) 2020; 32 2119_CR49 2119_CR47 2119_CR46 C Li (2119_CR190) 2022; 35 Z Tang (2119_CR183) 2022; 34
References_xml	– reference: Arivazhagan MG, Aggarwal V, Singh AK, Choudhary S (2019) Federated learning with personalization layers. arXiv: 1912.00818 [cs.LG] – reference: Shang X, Huang G, Lu Y, Lou J, Han B, Cheung Y-m, Wang H (2023) Federated semi-supervised learning with annotation heterogeneity. arXiv preprint arXiv:2303.02445 – reference: Rong D, He Q, Chen J ( 2022) Poisoning deep learning based recommender model in federated learning scenarios. In: Raedt LD (ed) Proceedings of the thirty-first international joint conference on artificial intelligence, IJCAI-22, pp 2204– 2210 . Main Track. https://doi.org/10.24963/ijcai.2022/306 – reference: LongGXieMShenTZhouTWangXJiangJMulti-center federated learning: clients clustering for better personalizationWorld Wide Web2023261481500 – reference: Liang PP, Liu T, Ziyin L, Salakhutdinov R, Morency L-P (2020) Think locally, act globally: federated learning with local and global representations. Adv Neural Inf Process Syst – reference: WatkinsCJDayanPQ-learningMach Learn19928279292 – reference: Li T, Sanjabi M, Beirami A, Smith V (2020) Fair resource allocation in federated learning. In: International conference on learning representations – reference: ChenYQinXWangJYuCGaoWFedhealth: a federated transfer learning framework for wearable healthcareIEEE Intell Syst20203548393 – reference: AlawadMYoonH-JGaoSMumphreyBWuX-CDurbinEBJeongJCHandsIRustDCoyleLPrivacy-preserving deep learning nlp models for cancer registriesIEEE Trans Emerg Top Comput20209312191230 – reference: DinhCTVuTTTranNHDaoMNZhangHA new look and convergence rate of federated multitask learning with Laplacian regularizationIEEE Trans Neural Netw Learn Syst202210.1109/TNNLS.2022.3224252 – reference: Huang Y, Chu L, Zhou Z, Wang L, Liu J, Pei J, Zhang Y (2021) Personalized cross-silo federated learning on non-iid data. In: AAAI conference on artificial intelligence – reference: MaiWYaoJChenGZhangYCheungY-MHanBServer-client collaborative distillation for federated reinforcement learningACM Trans Knowl Discov Data2023181122 – reference: TanYLongGLIULZhouTLuQJiangJZhangCFedproto: federated prototype learning across heterogeneous clientsProc AAAI Conf Artif Intell20223688432844010.1609/aaai.v36i8.20819 – reference: KangYLiuYLiangXFedcvt: semi-supervised vertical federated learning with cross-view trainingACM Trans Intell Syst Technol (TIST)2022134116 – reference: ZhuZHongJDrewSZhouJResilient and communication efficient learning for heterogeneous federated systemsProc Mach Learn Res202216227504 – reference: Chen H-Y, Chao W-L (2020) FedBE: making bayesian model ensemble applicable to federated learning. In: International conference on learning representations – reference: Yu FX, Rawat AS, Menon AK, Kumar S (2020) Federated learning with only positive labels. In: International conference on machine learning – reference: Jiang M, Yang H, Li X, Liu Q, Heng P-A, Dou Q ( 2022) Dynamic bank learning for semi-supervised federated image diagnosis with class imbalance. In: International conference on medical image computing and computer-assisted intervention, Springer, pp 196–206 – reference: Tolpegin V, Truex S, Gursoy ME, Liu L (2020) Data poisoning attacks against federated learning systems. In: Computer security–ESORICS 2020: 25th European Symposium on research in computer security, ESORICS 2020, Guildford, UK, September 14–18, 2020, Proceedings, Part I 25, Springer, pp 480–501 – reference: SuttonRSLearning to predict by the methods of temporal differencesMach Learn19883944 – reference: YangYYangRPengHLiYLiTLiaoYZhouPFedACK: federated adversarial contrastive knowledge distillation for cross-lingual and cross-model social bot detectionProc ACM Web Conf2023202313141323 – reference: Zhang X, Chen X, Hong M, Wu S, Yi J (2022) Understanding clipping for federated learning: convergence and client-level differential privacy. In: Chaudhuri K, Jegelka S, Song L, Szepesvari C, Niu G, Sabato S (eds) Proceedings of the 39th international conference on machine learning. Proceedings of machine learning research, vol 162, pp 26048–26067. https://proceedings.mlr.press/v162/zhang22b.html – reference: Lyu L, Yu H, Yang Q (2020) Threats to federated learning: a survey. arXiv preprint arXiv:2003.02133 – reference: SohnKBerthelotDCarliniNZhangZZhangHRaffelCACubukEDKurakinALiC-LFixmatch: simplifying semi-supervised learning with consistency and confidenceAdv Neural Inf Process Syst202033596608 – reference: Yurochkin M, Agarwal M, Ghosh S, Greenewald K, Hoang N, Khazaeni Y (2019) Bayesian nonparametric federated learning of neural networks. In: International conference on machine learning, pp 7252–7261 – reference: JinXBuJYuZZhangHWangYFedCrack: federated transfer learning with unsupervised representation for crack detectionIEEE Trans Intell Transp Syst202310.1109/TITS.2023.3286439 – reference: Caldas S, Smith V, Talwalkar A (2018) Federated kernelized multi-task learning. In: Conference on machine learning and systems – reference: MarfoqONegliaGBelletAKameniLVidalRFederated multi-task learning under a mixture of distributionsAdv Neural Inf Process Syst2021341543415447 – reference: ChenYSunXJinYCommunication-efficient federated deep learning with layerwise asynchronous model update and temporally weighted aggregationIEEE Trans Neural Netw Learn Syst2020311042294238 – reference: SunZWeiEA communication-efficient algorithm with linear convergence for federated minimax learningAdv Neural Inf Process Syst20223560606073 – reference: Bietti A, Wei C-Y, Dudik M, Langford J, Wu S (2022) Personalization improves privacy-accuracy tradeoffs in federated learning. In: Chaudhuri K, Jegelka S, Song L, Szepesvari C, Niu G, Sabato S (eds) Proceedings of the 39th international conference on machine learning. Proceedings of machine learning research, vol 162, pp 1945–1962. https://proceedings.mlr.press/v162/bietti22a.html – reference: Tang Z, Chu X, Ran RY, Lee S, Shi S, Zhang Y, Wang Y, Liang AQ, Avestimehr S, He C (2023) Fedml parrot: a scalable federated learning system via heterogeneity-aware scheduling on sequential and hierarchical training. arXiv preprint arXiv:2303.01778 – reference: Jiang Y, Konečnỳ J, Rush K, Kannan S (2019) Improving federated learning personalization via model agnostic meta learning. In: Advances in neural information processing systems workshop – reference: Durmus AE, Yue Z, Ramon M, Matthew M, Paul W, Venkatesh S (2021) Federated learning based on dynamic regularization. In: International conference on learning representations – reference: Lubana E, Tang CI, Kawsar F, Dick R, Mathur A (2022) Orchestra: unsupervised federated learning via globally consistent clustering. In: International conference on machine learning, PMLR, pp 14461–14484 – reference: Zhuo HH, Feng W, Xu Q, Yang Q, Lin Y (2019) Federated deep reinforcement learning. arXiv preprint arXiv:1901.08277 – reference: HongJWangHWangZZhouJFederated robustness propagation: sharing adversarial robustness in heterogeneous federated learningProc AAAI Conf Artif Intell20233778937901 – reference: Chen X, He K (2021) Exploring simple siamese representation learning. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 15750–15758 – reference: Liu Y, Pan S, Jin M, Zhou C, Xia F, Yu PS (2021) Graph self-supervised learning: a survey. arXiv preprint arXiv:2103.00111 – reference: CaoXJiaJGongNZProvably secure federated learning against malicious clientsProc AAAI Conf Artif Intell20213586885689310.1609/aaai.v35i8.16849 – reference: XiaoPChengSBayesian federated neural matching that completes full informationProc AAAI Conf Artif Intell2023371047310480 – reference: ChaiDWangLChenKYangQSecure federated matrix factorizationIEEE Intell Syst20203651120 – reference: Kim J, Kim G, Han B (2022) Multi-level branched regularization for federated learning. In: International conference on machine learning, PMLR, pp 11058–11073 – reference: Tan Y, Chen C, Zhuang W, Dong X, Lyu L, Long G (2023) Is heterogeneity notorious? taming heterogeneity to handle test-time shift in federated learning. In: Thirty-seventh conference on neural information processing systems – reference: Zhu L, Lin H, Lu Y, Lin Y, Han S (2021) Delayed gradient averaging: tolerate the communication latency for federated learning. In: Ranzato M, Beygelzimer A, Dauphin Y, Liang PS, Vaughan JW (eds) Advances in neural information processing systems, vol 34, pp 29995–30007. https://proceedings.neurips.cc/paper/2021/file/fc03d48253286a798f5116ec00e99b2b-Paper.pdf – reference: YangHHeHZhangWCaoXFedSteg: a federated transfer learning framework for secure image steganalysisIEEE Trans Netw Sci Eng20208210841094 – reference: QiTWuFWuCLyuLXuTLiaoHYangZHuangYXieXFairvfl: a fair vertical federated learning framework with contrastive adversarial learningAdv Neural Inf Process Syst20223578527865 – reference: Jin H, Peng Y, Yang W, Wang S, Zhang Z (2022) Federated reinforcement learning with environment heterogeneity. In: International conference on artificial intelligence and statistics, PMLR, pp 18–37 – reference: LiuJWangJHRongCXuYYuTWangJFedpa: an adaptively partial model aggregation strategy in federated learningComput Netw2021199108468 – reference: Singh I, Zhou H, Yang K, Ding M, Lin B, Xie P (2020) Differentially-private federated neural architecture search. In: FL-international conference on machine learning workshop – reference: Chen R, Wan Q, Prakash P, Zhang L, Yuan X, Gong Y, Fu X, Pan M (2023) Workie-talkie: accelerating federated learning by overlapping computing and communications via contrastive regularization. In: Proceedings of the IEEE/CVF international conference on computer vision, pp 16999–17009 – reference: He C, Li S, So J, Zhang M, Wang H, Wang X, Vepakomma P, Singh A, Qiu H, Shen L, Zhao P, Kang Y, Liu Y, Raskar R, Yang Q, Annavaram M, Avestimehr S (2020) FedML: a research library and benchmark for federated machine learning. arXiv preprint arXiv:2007.13518 – reference: WangHSreenivasanKRajputSVishwakarmaHAgarwalSSohnJ-YLeeKPapailiopoulosDAttack of the tails: yes, you really can backdoor federated learningAdv Neural Inf Process Sys2020331607016084 – reference: Konečnỳ J, McMahan HB, Yu FX, Richtárik P, Suresh AT, Bacon D (2016) Federated learning: strategies for improving communication efficiency. arXiv preprint arXiv:1610.05492 – reference: Li Q, Wen Z, He B (2019) Federated learning systems: vision, hype and reality for data privacy and protection. arXiv preprint arXiv:1907.09693 – reference: Karimireddy SP, Kale S, Mohri M, Reddi SJ, Stich SU, Suresh AT (2020) Scaffold: stochastic controlled averaging for federated learning. In: International conference on machine learning, pp 5132–5143 – reference: Wang H, Yurochkin M, Sun Y, Papailiopoulos D, Khazaeni Y (2020) Federated learning with matched averaging. In: International conference on learning representations – reference: ZhaoBSunPWangTJiangKFedinv: byzantine-robust federated learning by inversing local model updatesProc AAAI Conf Artif Intell20223689171917910.1609/aaai.v36i8.20903 – reference: Tan AZ, Yu H, Cui L, Yang Q (2021) Towards personalized federated learning. arXiv preprint arXiv:2103.00710 – reference: ThapaCMahawaga ArachchigePCCamtepeSSunLSplitfed: when federated learning meets split learningProc AAAI Conf Artif Intell20223688485849310.1609/aaai.v36i8.20825 – reference: LinTKongLStichSUJaggiMEnsemble distillation for robust model fusion in federated learningAdv Neural Inf Process Syst20203323512363 – reference: Fan FX, Ma Y, Dai Z, Tan C, Low BKH (2023) Fedhql: federated heterogeneous q-learning. In: Proceedings of the 2023 international conference on autonomous agents and multiagent systems, pp 2810–2812 – reference: WeiX-XHuangHBalanced federated semi-supervised learning with fairness-aware pseudo-labelingIEEE Trans Neural Netw Learn Syst202310.1109/TNNLS.2022.3233093 – reference: Long G, Tan Y, Jiang J, Zhang C (2020) Federated learning for open banking. In: Federated learning: privacy and incentive, pp 240–254 – reference: He K, Fan H, Wu Y, Xie S, Girshick R (2020) Momentum contrast for unsupervised visual representation learning. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 9729–9738 – reference: Xie Y, Zhang W, Pi R, Wu F, Chen Q, Xie X, Kim S (2022) Robust federated learning against both data heterogeneity and poisoning attack via aggregation optimization. arXiv preprint – reference: ZhangFKuangKChenLYouZShenTXiaoJZhangYWuCWuFZhuangYFederated unsupervised representation learningFront Inf Technol Electron Eng202324811811193 – reference: ZhangYZhangWPuLLinTYanJTo distill or not to distill: towards fast, accurate and communication efficient federated distillation learningIEEE Internet of Things J202310.1109/JIOT.2023.3324666 – reference: Zhang J, Li B, Chen C, Lyu L, Wu S, Ding S, Wu C (2023) Delving into the adversarial robustness of federated learning. In: Proceedings of the AAAI conference on artificial intelligence – reference: Lu Y, Chen L, Zhang Y, Zhang Y, Han B, Cheung Y-m, Wang H (2023) Federated learning with extremely noisy clients via negative distillation. arXiv preprint arXiv:2312.12703 – reference: Grammenos A, Mendoza Smith R, Crowcroft J, Mascolo C (2020) Federated principal component analysis. In: Advances in neural information processing systems – reference: Rehman YAU, Gao Y, Gusmao PPB, Alibeigi M, Shen J, Lane ND (2023) L-DAWA: layer-wise divergence aware weight aggregation in federated self-supervised visual representation learning. In: Proceedings of the IEEE/CVF international conference on computer vision, pp 16464–16473 – reference: He C, Annavaram M, Avestimehr S (2020) FedNAS: federated deep learning via neural architecture search. In: Proceedings of the IEEE conference on computer vision and pattern recognition – reference: Jeong E, Oh S, Kim H, Park J, Bennis M, Kim S-L (2018) Communication-efficient on-device machine learning: Federated distillation and augmentation under non-IID private data. In: Advances in neural information processing systems – reference: Li D, Wang J (2019) FedMD: heterogenous federated learning via model distillation. In: Advances in neural information processing systems workshop – reference: McMahan HB, Moore E, Ramage D, Hampson S, et al (2017) Communication-efficient learning of deep networks from decentralized data. In: International Conference on artificial intelligence and statistics, pp 1273–1282 – reference: He C, Tan C, Tang H, Qiu S, Liu J (2019) Central server free federated learning over single-sided trust social networks. arXiv preprint arXiv:1910.04956 – reference: Reddi S, Charles Z, Zaheer M, Garrett Z, Rush K, Konečnỳ J, Kumar S, McMahan HB (2021) Adaptive federated optimization. In: International conference on learning representations – reference: Pan Q, Zhu Y (2022) Fedwalk: communication efficient federated unsupervised node embedding with differential privacy. In: Proceedings of the 28th ACM SIGKDD conference on knowledge discovery and data mining, pp 1317–1326 – reference: TanYLiuYLongGJiangJLuQZhangCFederated learning on non-iid graphs via structural knowledge sharingProc AAAI Conf Artif Intel20233799539961 – reference: Hu R, Gong Y, Guo Y ( 2021) Federated learning with sparsification-amplified privacy and adaptive optimization. In: Zhou Z-H (ed) Proceedings of the thirtieth international joint conference on artificial intelligence, IJCAI-21, pp 1463–1469 . Main Track. https://doi.org/10.24963/ijcai.2021/202 – reference: HeCAnnavaramMAvestimehrSGroup knowledge transfer: federated learning of large cnns at the edgeAdv Neural Inf Process Syst2020331406814080 – reference: Li X, Huang K, Yang W, Wang S, Zhang Z (2020) On the convergence of Fedavg on non-iid data. In: International conference on learning representations – reference: LiangFPanWMingZFedrec++: Lossless federated recommendation with explicit feedbackProc AAAI Conf Artif Intel20213542244231 – reference: Chen T, Kornblith S, Norouzi M, Hinton G (2020) A simple framework for contrastive learning of visual representations. In: International conference on machine learning, PMLR, pp 1597–1607 – reference: HijaziNMAloqailyMGuizaniMOuniBKarrayFSecure federated learning with fully homomorphic encryption for IoT communicationsIEEE Internet of Things J202310.1109/JIOT.2023.3302065 – reference: WangBYuanZYingYYangTMemory-based optimization methods for model-agnostic meta-learning and personalized federated learningJ Mach Learn Res2023241464596092 – reference: DiaoEDingJTarokhVSemifl: semi-supervised federated learning for unlabeled clients with alternate trainingAdv Neural Inf Process Syst2022351787117884 – reference: Yang Z, Zhang Y, Zheng Y, Tian X, Peng H, Liu T, Han B (2023) FedFed: feature distillation against data heterogeneity in federated learning. In: Thirty-seventh conference on neural information processing systems – reference: QiuDXueJZhangTWangJSunMFederated reinforcement learning for smart building joint peer-to-peer energy and carbon allowance tradingAppl Energy2023333120526 – reference: Yuan W, Yin H, Wu F, Zhang S, He T, Wang H (2023) Federated unlearning for on-device recommendation. In: Proceedings of the sixteenth ACM international conference on web search and data mining, pp 393–401 – reference: SattlerFMüllerK-RSamekWClustered federated learning: model-agnostic distributed multitask optimization under privacy constraintsIEEE Trans Neural Netw Learn Syst2020328371037224296542 – reference: Peng X, Huang Z, Zhu Y, Saenko K (2020) Federated adversarial domain adaptation. In: International conference on learning representations – reference: Ji S, Pan S, Long G, Li X, Jiang J, Huang Z (2019) Learning private neural language modeling with attentive aggregation. In: International joint conference on neural network – reference: Smith V, Chiang C-K, Sanjabi M, Talwalkar AS (2017) Federated multi-task learning. In: Advances in neural information processing systems, pp 4427– 4437 – reference: Yapp AZH, Koh HSN, Lai YT, Kang J, Li X, Ng JS, Jiang H, Lim WYB, Xiong Z, Niyato D ( 2021) Communication-efficient and scalable decentralized federated edge learning. In: Zhou Z-H (ed) Proceedings of the thirtieth international joint conference on artificial intelligence, IJCAI-21, pp 5032– 5035 . https://doi.org/10.24963/ijcai.2021/720 . Demo Track. https://doi.org/10.24963/ijcai.2021/720 – reference: Mansour Y, Mohri M, Ro J, Suresh AT (2020) Three approaches for personalization with applications to federated learning. arXiv preprint arXiv:2002.10619 – reference: WangKLiJWuWAn efficient intrusion detection method based on federated transfer learning and an extreme learning machine with privacy preservationSecur Commun Netw202210.1155/2022/2913293 – reference: Cai L, Chen N, Cao Y, He J, Li Y (2023) FedCE: personalized federated learning method based on clustering ensembles. In: Proceedings of the 31st ACM international conference on multimedia, pp 1625–1633 – reference: Wang H, Kaplan Z, Niu D, Li B (2020) Optimizing federated learning on non-IID data with reinforcement learning. In: IEEE international conference on computer communications, IEEE, pp 1698–1707 – reference: ChenZLiWXingXYuanYMedical federated learning with joint graph purification for noisy label learningMed Image Anal202390102976 – reference: ChenCLiuYMaXLyuLCalfat: calibrated federated adversarial training with label skewnessAdv Neural Inf Process Syst20223535693581 – reference: Ma Y, Xie Z, Wang J, Chen K, Shou L (2022) Continual federated learning based on knowledge distillation. In: Raedt LD (ed) Proceedings of the thirty-first international joint conference on artificial intelligence, IJCAI, vol 22, pp 2182–2188. (Main Track.). https://doi.org/10.24963/ijcai.2022/303 – reference: Dennis DK, Li T, Smith V (2021) Heterogeneity for the win: one-shot federated clustering. In: International conference on machine learning, PMLR, pp 2611–2620 – reference: Tang Z, Zhang Y, Shi S, He X, Han B, Chu X ( 2022) Virtual homogeneity learning: defending against data heterogeneity in federated learning. In: International conference on machine learning, PMLR, pp 21111–21132 – reference: Deng Y, Kamani MM, Mahdavi M (2020) Adaptive personalized federated learning. arXiv:2003:13461 – reference: Bagdasaryan E, Veit A, Hua Y, Estrin D, Shmatikov V (2020) How to backdoor federated learning. In: International conference on artificial intelligence and statistics, PMLR, pp 2938–2948 – reference: Wang Y, Lin L, Chen J ( 2022) Communication-efficient adaptive federated learning. In: Chaudhuri K, Jegelka S, Song L, Szepesvari C, Niu G, Sabato S (eds) Proceedings of the 39th international conference on machine learning. Proceedings of machine learning research, vol 162, pp 22802–22838 . https://proceedings.mlr.press/v162/wang22o.html – reference: CheSKongZPengHSunLLeowAChenYHeLFederated multi-view learning for private medical data integration and analysisACM Trans Intell Syst Technol (TIST)2022134123 – reference: Mohri M, Sivek G, Suresh AT (2019) Agnostic federated learning. In: International conference on machine learning – reference: LiuZYangLFanZPengHYuPSFederated social recommendation with graph neural networkACM Trans Intell Syst Technol (TIST)2022134124 – reference: Li Q, He B, Song D (2021) Model-contrastive federated learning. arXiv: 2103.16257 [cs.LG] – reference: Ji S, Jiang W, Walid A, Li X (2020) Dynamic sampling and selective masking for communication-efficient federated learning. arXiv preprint arXiv:2003.09603 – reference: Isik B, Pase F, Gunduz D, Weissman T, Michele Z: Sparse random networks for communication-efficient federated learning. In: The Eleventh international conference on learning representations (2022) – reference: Lin Y, Ren P, Chen Z, Ren Z, Yu D, Ma J, Rijke Md, Cheng X (2020) Meta matrix factorization for federated rating predictions. In: SIGIR, pp 981– 990 – reference: Xu J, Wang F (2019) Federated learning for healthcare informatics. arXiv preprint arXiv:1911.06270 – reference: FengSLiBYuHLiuYYangQSemi-supervised federated heterogeneous transfer learningKnowl-Based Syst2022252109384 – reference: Zhang Z, Jiang Y, Shi Y, Shi Y, Chen W ( 2022) Federated reinforcement learning for real-time electric vehicle charging and discharging control. In: 2022 IEEE Globecom workshops (GC Wkshps), IEEE, pp 1717–1722 – reference: ZhuMChenZYuanYFedDM: federated weakly supervised segmentation via annotation calibration and gradient de-conflictingIEEE Trans Med Imag202310.1109/TMI.2023.3235757 – reference: Liang X, Lin Y, Fu H, Zhu L, Li, X ( 2022) Rscfed: random sampling consensus federated semi-supervised learning. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 10154–10163 – reference: Chung J, Lee K, Ramchandran K (2022) Federated unsupervised clustering with generative models. In: AAAI 2022 international workshop on trustable, verifiable and auditable federated learning – reference: Jeong W, Yoon J, Yang E, Hwang SJ (2021) Federated semi-supervised learning with inter-client consistency & disjoint learning. In: International conference on learning representations – reference: Cheng A, Wang P, Zhang XS, Cheng J (2022) Differentially private federated learning with local regularization and sparsification. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 10122–10131 – reference: Tang Z, Wang Y, He X, Zhang L, Pan X, Wang Q, Zeng R, Zhao K, Shi S, He B, et al (2023) Fusionai: decentralized training and deploying llms with massive consumer-level gpus. arXiv preprint arXiv:2309.01172 – reference: WuZSunSWangYLiuMPanQJiangXGaoBFedICT: federated multi-task distillation for multi-access edge computingIEEE Trans Parallel Distrib Syst202310.1109/TPDS.2023.3289444 – reference: WangXLiRWangCLiXTalebTLeungVCAttention-weighted federated deep reinforcement learning for device-to-device assisted heterogeneous collaborative edge cachingIEEE J Sel Areas Commun2020391154169 – reference: ZawadSAliAChenP-YAnwarAZhouYBaracaldoNTianYYanFCurse or redemption? how data heterogeneity affects the robustness of federated learningProc AAAI Conf Artif Intell20213512108071081410.1609/aaai.v35i12.17291 – reference: Rasouli M, Sun T, Rajagopal R (2020) FedGAN: federated generative adversarial networks for distributed data. arXiv preprint arXiv:2006.07228 – reference: Fan C, Liu P (2020) Federated generative adversarial learning. arXiv preprint arXiv:2005.03793 – reference: Xiao J, Du C, Duan Z, Guo W (2021) A novel server-side aggregation strategy for federated learning in non-iid situations. In: 2021 20th International symposium on parallel and distributed computing (ISPDC), IEEE, pp 17–24 – reference: CaoXLiZSunGYuHGuizaniMCross-silo heterogeneous model federated multitask learningKnowl-Based Syst2023265110347 – reference: Li M, Li Q, Wang Y (2023) Class balanced adaptive pseudo labeling for federated semi-supervised learning. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 16292–16301 – reference: Zhang X, Li Y, Li W, Guo K, Shao Y (2022) Personalized federated learning via variational bayesian inference. In: International conference on machine learning, PMLR, pp 26293–26310 – reference: Kairouz P, McMahan HB, Avent B, Bellet A, Bennis M, Bhagoji AN, Bonawitz K, Charles Z, Cormode G, Cummings R, et al (2019) Advances and open problems in federated learning. arXiv preprint arXiv:1912.04977 – reference: Lin S, Yang L, He Z, Fan D, Zhang J (2021) Metagater: fast learning of conditional channel gated networks via federated meta-learning. In: 2021 IEEE 18th international conference on mobile Ad Hoc and smart systems (MASS), IEEE, pp 164– 172 – reference: Yi L, Gang W, Xiaoguang L (2022) QSFL: a two-level uplink communication optimization framework for federated learning. In: Chaudhuri K, Jegelka S, Song L, Szepesvari C, Niu G, Sabato S (eds) Proceedings of the 39th international conference on machine learning. Proceedings of machine learning research, vol 162, pp 25501–25513. https://proceedings.mlr.press/v162/yi22a.html – reference: Augenstein S, McMahan HB, Ramage D, Ramaswamy S, Kairouz P, Chen M, Mathews R, Arcas BA (2020) Generative models for effective ml on private, decentralized datasets. In: International conference on learning representations – reference: YangQLiuYChenTTongYFederated machine learning: concept and applicationsACM Trans Intell Syst Technol201910212 – reference: LiuYKangYXingCChenTYangQA secure federated transfer learning frameworkIEEE Intell Syst2020357082 – reference: Chen J, Zhang A ( 2022) FedMSplit: correlation-adaptive federated multi-task learning across multimodal split networks. In: Proceedings of the 28th ACM SIGKDD Conference on knowledge discovery and data mining, pp 87– 96 – reference: Fallah A, Mokhtari A, Ozdaglar A (2020) Personalized federated learning with theoretical guarantees: a model-agnostic meta-learning approach. In: Advances in neural information processing systems – reference: EzzeldinY.HYanSHeCFerraraEAvestimehrA. SFairfed: Enabling group fairness in federated learningProc AAAI Conf Artif Intell20233774947502 – reference: Li T, Sahu AK, Zaheer M, Sanjabi M, Talwalkar A, Smith V (2020) Federated optimization in heterogeneous networks. In: Conference on machine learning and systems – reference: Lyu L, Xu X, Wang Q, Yu H (2020) Collaborative fairness in federated learning. Federated Learning: privacy and Incentive, pp. 189–204 – reference: Peng H, Li H, Song Y, Zheng V, Li J (2021) Differentially private federated knowledge graphs embedding. In: Proceedings of the 30th ACM international conference on information & knowledge management, pp 1416–1425 – reference: FanKHongJLiWZhaoXLiHYangYFlsg: a novel defense strategy against inference attacks in vertical federated learningIEEE Internet of Things J202310.1109/JIOT.2023.3302792 – reference: YangYGuanZLiJZhaoWCuiJWangQInterpretable and efficient heterogeneous graph convolutional networkIEEE Trans Knowl Data Eng202135216371650 – reference: GrillJ-BStrubFAltchéFTallecCRichemondPBuchatskayaEDoerschCAvila PiresBGuoZGheshlaghi AzarMBootstrap your own latent-a new approach to self-supervised learningAdv Neural Inf Process Syst2020332127121284 – reference: HeCCeyaniEBalasubramanianKAnnavaramMAvestimehrSSpreadGNN: decentralized multi-task federated learning for graph neural networks on molecular dataProc AAAI Conf Artif Intell20223668656873 – reference: Sun L, Qian J, Chen X (2021) LDP-FL: practical private aggregation in federated learning with local differential privacy. In: Zhou Z-H (ed) Proceedings of the Thirtieth international joint conference on artificial intelligence, IJCAI-21, pp 1571–1578 . Main Track. https://doi.org/10.24963/ijcai.2021/217 – reference: LiCWangHCommunication efficient federated learning for generalized linear banditsAdv Neural Inf Process Syst2022353841138423 – reference: Jin Y, Wei X, Liu Y, Yang Q (2020) A survey towards federated semi-supervised learning. arXiv preprint arXiv:2002.11545 – reference: Jiang J, Ji S, Long G (2020) Decentralized knowledge acquisition for mobile internet applications. World Wide Web – reference: Briggs C, Fan Z, Andras P (2020) Federated learning with hierarchical clustering of local updates to improve training on non-iid data. In: International joint conference on neural network – reference: Chen X, Fan H, Girshick R, He K (2020) Improved baselines with momentum contrastive learning. arXiv preprint arXiv:2003.04297 – reference: LiuZChenYZhaoYYuHLiuYBaoRJiangJNieZXuQYangQContribution-aware federated learning for smart healthcareProc AAAI Conf Artif Intel2022361239612404 – reference: YangXSongZKingIXuZA survey on deep semi-supervised learningIEEE Trans Knowl Data Eng202235989348954 – reference: KhalatbarisoltaniABoulonLHuXIntegrating model predictive control with federated reinforcement learning for decentralized energy management of fuel cell vehiclesIEEE Trans Intell Transp Syst202310.1109/TITS.2023.3303991 – reference: ZhuHZhangHJinYFrom federated learning to federated neural architecture search: a surveyComplex Intell Syst202072639657 – reference: Lo SK, Lu Q, Wang C, Paik H, Zhu L (2020) A systematic literature review on federated machine learning: from a software engineering perspective. arXiv preprint arXiv:2007.11354 – reference: Hoang M, Hoang N, Low BKH, Kingsford C ( 2019) Collective model fusion for multiple black-box experts. In: International conference on machine learning, PMLR, pp 2742–2750 – reference: Ji S, Long G, Pan S, Zhu T, Jiang J, Wang S, Li X(2019) Knowledge transferring via model aggregation for online social care. arXiv preprint arXiv:1905.07665 – reference: Ghosh A, Chung J, Yin D, Ramchandran K (2020) An efficient framework for clustered federated learning. In: Advances in neural information processing systems – reference: WuXLiangZWangJFedMed: a federated learning framework for language modelingSensors202020144048 – reference: Papernot N, Abadi M, Erlingsson Ú, Goodfellow I, Talwar K (2017) Semi-supervised knowledge transfer for deep learning from private training data. In: International conference on learning representations – reference: Zhang L, Shen L, Ding L, Tao D, Duan L-Y (2022) Fine-tuning global model via data-free knowledge distillation for non-iid federated learning. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 10174–10183 – reference: Park J, Han D-J, Choi M, Moon J (2021) Sageflow: robust federated learning against both stragglers and adversaries. In: Ranzato M, Beygelzimer A, Dauphin Y, Liang PS, Vaughan JW (eds) Advances in neural information processing systems, vol 34, pp 840–851. https://proceedings.neurips.cc/paper/2021/file/076a8133735eb5d7552dc195b125a454-Paper.pdf – reference: Zhan Y, Zhang J (2020) An incentive mechanism design for efficient edge learning by deep reinforcement learning approach. In: IEEE international conference on computer communications, IEEE, pp 2489–2498 – reference: OzdayiMSKantarciogluMGelYRDefending against backdoors in federated learning with robust learning rateProc AAAI Conf Artif Intell202135109268927610.1609/aaai.v35i10.17118 – reference: XuXPengHBhuiyanMZAHaoZLiuLSunLHeLPrivacy-preserving federated depression detection from multisource mobile health dataIEEE Trans Industr Inf202118747884797 – reference: SinghSPJaggiMModel fusion via optimal transportAdv Neural Inf Process Syst2020332204522055 – reference: Li X, Song Z, Yang J (2023) Federated adversarial learning: a framework with convergence analysis. In: International conference on machine learning, PMLR, pp 19932–19959 – reference: Zhang Z, Panda A, Song L, Yang Y, Mahoney M, Mittal P, Kannan R, Gonzalez J (2022) Neurotoxin: durable backdoors in federated learning. In: Proceedings of the 39th international conference on machine learning, vol 162, pp 26429–26446 – reference: GuoQQiYQiSWuDLiQFedmcsa: personalized federated learning via model components self-attentionNeurocomputing2023560126831 – reference: Jin X, Chen P-Y, Hsu C-Y, Yu C-M, Chen T (2021) CAFE: catastrophic data leakage in vertical federated learning. In: Ranzato M, Beygelzimer A, Dauphin Y, Liang PS, Vaughan JW (eds) Advances in neural information processing systems, vol 34, pp 994–1006. https://proceedings.neurips.cc/paper/2021/file/08040837089cdf46631a10aca5258e16-Paper.pdf – reference: Yao X, Huang T, Zhang R-X, Li R, Sun L (2019) Federated learning with unbiased gradient aggregation and controllable meta updating. In: Advances in neural information processing systems workshop – reference: CuiYCaoKZhouJWeiTOptimizing training efficiency and cost of hierarchical federated learning in heterogeneous mobile-edge cloud computingIEEE Tran Comput-Aid Des Integr Circuits Syst202210.1109/TCAD.2022.3205551 – reference: Park S, Han S, Wu F, Kim S, Zhu B, Xie X, Cha M ( 2023) Feddefender: client-side attack-tolerant federated learning. In: Proceedings of the 29th ACM SIGKDD conference on knowledge discovery and data mining, pp 1850–1861 – reference: Zhu Z, Si S, Wang J, Xiao J (2022) Cali3f: calibrated fast fair federated recommendation system. In: 2022 international joint conference on neural networks (IJCNN), IEEE, pp 1–8 – reference: Zhuang W, Wen Y, Zhang S (2022) Divergence-aware federated self-supervised learning. In: International conference on learning representations – reference: Han S, Park S, Wu F, Kim S, Wu C, Xie X, Cha M( 2022) Fedx: unsupervised federated learning with cross knowledge distillation. In: European conference on computer vision, Springer, pp 691–707 – reference: LiuRCaoYChenHGuoRYoshikawaMFlame: differentially private federated learning in the shuffle modelProc AAAI Conf Artif Intell202135108688869610.1609/aaai.v35i10.17053 – reference: LiTSahuAKTalwalkarASmithVFederated learning: challenges, methods, and future directionsIEEE Sign Process Mag20203735060 – reference: Wen Y, Geiping JA, Fowl L, Goldblum M, Goldstein T (2022) In: Chaudhuri K, Jegelka S, Song L, Szepesvari C, Niu G, Sabato S (eds) Proceedings of the 39th international conference on machine learning. Proceedings of machine learning research. Fishing for user data in large-batch federated learning via gradient magnification, vol 162, pp 23668–23684. https://proceedings.mlr.press/v162/wen22a.html – reference: Wang K, Mathews R, Kiddon C, Eichner H, Beaufays F, Ramage D (2019) Federated evaluation of on-device personalization. arXiv preprint arXiv:1910.10252 – reference: Huang Y, Gupta S, Song Z, Li K, Arora S (2021) Evaluating gradient inversion attacks and defenses in federated learning. In: Ranzato M, Beygelzimer A, Dauphin Y, Liang PS, Vaughan JW (eds) Advances in neural information processing systems, vol 34, pp 7232–7241. https://proceedings.neurips.cc/paper/2021/file/3b3fff6463464959dcd1b68d0320f781-Paper.pdf – reference: Finn C, Abbeel P, Levine S (2017) Model-agnostic meta-learning for fast adaptation of deep networks. In: International conference on machine learning, pp 1126– 1135 – reference: Zheng K, Liu X, Zhu G, Wu X, Niu J (2022) ChannelFed: enabling personalized federated learning via localized channel attention. In: GLOBECOM 2022-2022 IEEE global communications conference, IEEE, pp 2987–2992 – reference: Bram Bv, Saeed A, Ozcelebi T (2020) Towards federated unsupervised representation learning. In: ACM EdgeSys, pp 31–36 – reference: Sun J, Li A, DiValentin L, Hassanzadeh A, Chen Y, Li H ( 2021) FL-WBC: enhancing robustness against model poisoning attacks in federated learning from a client perspective. In: Ranzato M, Beygelzimer A, Dauphin Y, Liang PS, Vaughan JW (eds) Advances in neural information processing systems, vol 34, pp 12613– 12624. https://proceedings.neurips.cc/paper/2021/file/692baebec3bb4b53d7ebc3b9fabac31b-Paper.pdf – reference: TanYLongGMaJLiuLZhouTJiangJFederated learning from pre-trained models: a contrastive learning approachAdv Neural Inf Process Syst2022351933219344 – reference: Lattimore T, Szepesvári C (2020) Bandit algorithms – reference: Khodadadian S, Sharma P, Joshi G, Maguluri ST (2022) Federated reinforcement learning: linear speedup under markovian sampling. In: International conference on machine learning, PMLR, pp 10997–11057 – reference: LimWYBLuongNCHoangDTJiaoYLiangY-CYangQNiyatoDMiaoCFederated learning in mobile edge networks: a comprehensive surveyIEEE Commun Surv Tutor202022320312063 – reference: Yeganeh Y, Farshad A, Navab N, Albarqouni S (2020) Inverse distance aggregation for federated learning with non-iid data. In: DCL workshop at MICCAI, pp 150–159 – reference: ChangYZhangKGongJQianHPrivacy-preserving federated learning via functional encryption, revisitedIEEE Trans Inf Forens Secur20231818551869 – reference: GaoHXuAHuangHOn the convergence of communication-efficient local sgd for federated learningProc AAAI Conf Artif Intel20213597510751810.1609/aaai.v35i9.16920 – reference: Liu S, Ge Y, Xu S, Zhang Y, Marian A (2022) Fairness-aware federated matrix factorization. In: Proceedings of the 16th ACM conference on recommender systems, pp 168–178 – reference: Agarwal N, Kairouz P, Liu Z (2021) The skellam mechanism for differentially private federated learning. In: Ranzato M, Beygelzimer A, Dauphin Y, Liang PS, Vaughan JW (eds) Advances in neural information processing systems, vol 34, pp 5052–5064. https://proceedings.neurips.cc/paper/2021/file/285baacbdf8fda1de94b19282acd23e2-Paper.pdf – reference: ChaHParkJKimHBennisMKimS-LProxy experience replay: federated distillation for distributed reinforcement learningIEEE Intell Syst202035494101 – reference: NiknamSDhillonHSReedJHFederated learning for wireless communications: motivation, opportunities, and challengesIEEE Commun Mag20205864651 – reference: ZhaoPCaoZJiangJGaoFPractical private aggregation in federated learning against inference attackIEEE Internet Things J2022101318329 – reference: Karimireddy SP, Jaggi M, Kale S, Mohri M, Reddi SJ, Stich SU, Suresh AT (2020) Mime: Mimicking centralized stochastic algorithms in federated learning. arXiv preprint arXiv:2008.03606 – reference: Muhammad K, Wang Q, O’Reilly-Morgan D, Tragos E, Smyth B, Hurley N, Geraci J, Lawlor A (2020) FedFast: going beyond average for faster training of federated recommender systems. In: SIGKDD, pp 1234–1242 – reference: GuptaSHuangYZhongZGaoTLiKChenDRecovering private text in federated learning of language modelsAdv Neural Inf Process Syst20223581308143 – reference: TangZShiSLiBChuXGossipfl: a decentralized federated learning framework with sparsified and adaptive communicationIEEE Trans Parallel Distrib Syst2022343909922 – reference: Wang H-P, Stich S, He Y, Fritz M (2022) Progfed: effective, communication, and computation efficient federated learning by progressive training. In: International conference on machine learning, PMLR, pp 23034–23054 – reference: Long G, Shen T, Tan Y, Gerrard L, Clarke A, Jiang J (2021) Federated learning for privacy-preserving open innovation future on digital health. In: Humanity driven AI: productivity, well-being, sustainability and partnership, pp 113–133 – volume: 20 start-page: 4048 issue: 14 year: 2020 ident: 2119_CR20 publication-title: Sensors doi: 10.3390/s20144048 – volume: 36 start-page: 11 issue: 5 year: 2020 ident: 2119_CR120 publication-title: IEEE Intell Syst doi: 10.1109/MIS.2020.3014880 – ident: 2119_CR208 – ident: 2119_CR37 – ident: 2119_CR134 – ident: 2119_CR163 – ident: 2119_CR169 doi: 10.1145/3459637.3482252 – volume: 18 start-page: 1855 year: 2023 ident: 2119_CR178 publication-title: IEEE Trans Inf Forens Secur doi: 10.1109/TIFS.2023.3255171 – volume: 35 start-page: 70 year: 2020 ident: 2119_CR48 publication-title: IEEE Intell Syst doi: 10.1109/MIS.2020.2988525 – ident: 2119_CR66 – ident: 2119_CR14 – volume: 36 start-page: 12396 year: 2022 ident: 2119_CR202 publication-title: Proc AAAI Conf Artif Intel – ident: 2119_CR100 doi: 10.1109/INFOCOM41043.2020.9155494 – ident: 2119_CR55 – ident: 2119_CR89 doi: 10.1609/aaai.v37i9.26331 – volume: 33 start-page: 22045 year: 2020 ident: 2119_CR119 publication-title: Adv Neural Inf Process Syst – ident: 2119_CR4 doi: 10.1007/978-3-030-58951-6_24 – ident: 2119_CR117 – volume: 13 start-page: 1 issue: 4 year: 2022 ident: 2119_CR201 publication-title: ACM Trans Intell Syst Technol (TIST) doi: 10.1145/3501816 – volume: 35 start-page: 7510 issue: 9 year: 2021 ident: 2119_CR181 publication-title: Proc AAAI Conf Artif Intel doi: 10.1609/aaai.v35i9.16920 – volume: 10 start-page: 12 issue: 2 year: 2019 ident: 2119_CR107 publication-title: ACM Trans Intell Syst Technol doi: 10.1145/3298981 – ident: 2119_CR175 doi: 10.1145/3580305.3599346 – volume: 35 start-page: 9268 issue: 10 year: 2021 ident: 2119_CR159 publication-title: Proc AAAI Conf Artif Intell doi: 10.1609/aaai.v35i10.17118 – ident: 2119_CR145 – ident: 2119_CR71 doi: 10.24963/ijcai.2022/303 – year: 2023 ident: 2119_CR73 publication-title: IEEE Internet of Things J doi: 10.1109/JIOT.2023.3324666 – year: 2022 ident: 2119_CR192 publication-title: IEEE Tran Comput-Aid Des Integr Circuits Syst doi: 10.1109/TCAD.2022.3205551 – volume: 33 start-page: 14068 year: 2020 ident: 2119_CR67 publication-title: Adv Neural Inf Process Syst – volume: 35 start-page: 94 issue: 4 year: 2020 ident: 2119_CR101 publication-title: IEEE Intell Syst doi: 10.1109/MIS.2020.2994942 – volume: 36 start-page: 8485 issue: 8 year: 2022 ident: 2119_CR157 publication-title: Proc AAAI Conf Artif Intell doi: 10.1609/aaai.v36i8.20825 – ident: 2119_CR25 – volume: 26 start-page: 481 issue: 1 year: 2023 ident: 2119_CR35 publication-title: World Wide Web doi: 10.1007/s11280-022-01046-x – ident: 2119_CR116 – volume: 33 start-page: 2351 year: 2020 ident: 2119_CR69 publication-title: Adv Neural Inf Process Syst – year: 2023 ident: 2119_CR204 publication-title: IEEE Trans Med Imag doi: 10.1109/TMI.2023.3235757 – volume: 37 start-page: 9953 year: 2023 ident: 2119_CR194 publication-title: Proc AAAI Conf Artif Intel – ident: 2119_CR135 doi: 10.1109/CVPR46437.2021.01549 – ident: 2119_CR207 – volume: 58 start-page: 46 issue: 6 year: 2020 ident: 2119_CR113 publication-title: IEEE Commun Mag doi: 10.1109/MCOM.001.1900461 – volume: 35 start-page: 83 issue: 4 year: 2020 ident: 2119_CR52 publication-title: IEEE Intell Syst doi: 10.1109/MIS.2020.2988604 – volume: 37 start-page: 50 issue: 3 year: 2020 ident: 2119_CR12 publication-title: IEEE Sign Process Mag doi: 10.1109/MSP.2020.2975749 – year: 2022 ident: 2119_CR32 publication-title: IEEE Trans Neural Netw Learn Syst doi: 10.1109/TNNLS.2022.3224252 – ident: 2119_CR60 doi: 10.1145/3534678.3539384 – ident: 2119_CR39 doi: 10.1145/3581783.3612217 – ident: 2119_CR42 – ident: 2119_CR95 – volume: 333 start-page: 120526 year: 2023 ident: 2119_CR140 publication-title: Appl Energy doi: 10.1016/j.apenergy.2022.120526 – ident: 2119_CR5 – volume: 32 start-page: 3710 issue: 8 year: 2020 ident: 2119_CR57 publication-title: IEEE Trans Neural Netw Learn Syst doi: 10.1109/TNNLS.2020.3015958 – ident: 2119_CR85 – ident: 2119_CR62 – ident: 2119_CR56 – ident: 2119_CR118 – volume: 35 start-page: 19332 year: 2022 ident: 2119_CR7 publication-title: Adv Neural Inf Process Syst – volume: 199 start-page: 108468 year: 2021 ident: 2119_CR18 publication-title: Comput Netw doi: 10.1016/j.comnet.2021.108468 – ident: 2119_CR141 doi: 10.1109/GCWkshps56602.2022.10008598 – volume: 35 start-page: 8130 year: 2022 ident: 2119_CR164 publication-title: Adv Neural Inf Process Syst – ident: 2119_CR131 doi: 10.1109/CVPR42600.2020.00975 – ident: 2119_CR6 – ident: 2119_CR173 – ident: 2119_CR30 doi: 10.1109/CVPR52688.2022.00988 – ident: 2119_CR9 doi: 10.1007/978-3-030-60548-3_15 – volume: 560 start-page: 126831 year: 2023 ident: 2119_CR23 publication-title: Neurocomputing doi: 10.1016/j.neucom.2023.126831 – ident: 2119_CR154 doi: 10.36227/techrxiv.12503420 – year: 2023 ident: 2119_CR139 publication-title: IEEE Trans Intell Transp Syst doi: 10.1109/TITS.2023.3303991 – volume: 31 start-page: 4229 issue: 10 year: 2020 ident: 2119_CR10 publication-title: IEEE Trans Neural Netw Learn Syst doi: 10.1109/TNNLS.2019.2953131 – year: 2022 ident: 2119_CR51 publication-title: Secur Commun Netw doi: 10.1155/2022/2913293 – ident: 2119_CR174 – ident: 2119_CR206 – ident: 2119_CR29 – ident: 2119_CR124 doi: 10.1609/aaai.v38i13.29329 – volume: 33 start-page: 21271 year: 2020 ident: 2119_CR133 publication-title: Adv Neural Inf Process Syst – ident: 2119_CR41 – volume: 9 start-page: 1219 issue: 3 year: 2020 ident: 2119_CR122 publication-title: IEEE Trans Emerg Top Comput doi: 10.1109/TETC.2020.2983404 – year: 2023 ident: 2119_CR179 publication-title: IEEE Internet of Things J doi: 10.1109/JIOT.2023.3302065 – volume: 162 start-page: 27504 year: 2022 ident: 2119_CR185 publication-title: Proc Mach Learn Res – ident: 2119_CR147 doi: 10.1109/CVPR46437.2021.01057 – year: 2023 ident: 2119_CR170 publication-title: IEEE Internet of Things J doi: 10.1109/JIOT.2023.3302792 – ident: 2119_CR96 – ident: 2119_CR68 – ident: 2119_CR186 doi: 10.24963/ijcai.2021/720 10.24963/ijcai.2021/720 – ident: 2119_CR102 doi: 10.1109/INFOCOM41043.2020.9155268 – ident: 2119_CR78 doi: 10.1007/978-3-031-16437-8_19 – ident: 2119_CR199 doi: 10.1145/3539597.3570463 – ident: 2119_CR123 – ident: 2119_CR146 – ident: 2119_CR98 doi: 10.1007/978-3-031-20056-4_40 – ident: 2119_CR106 – ident: 2119_CR19 doi: 10.1007/s11280-019-00775-w – volume: 35 start-page: 6060 year: 2022 ident: 2119_CR191 publication-title: Adv Neural Inf Process Syst – ident: 2119_CR63 – volume: 33 start-page: 16070 year: 2020 ident: 2119_CR2 publication-title: Adv Neural Inf Process Sys – ident: 2119_CR65 doi: 10.1145/3397271.3401081 – volume: 35 start-page: 3569 year: 2022 ident: 2119_CR90 publication-title: Adv Neural Inf Process Syst – volume: 37 start-page: 7893 year: 2023 ident: 2119_CR91 publication-title: Proc AAAI Conf Artif Intell – volume: 35 start-page: 8934 issue: 9 year: 2022 ident: 2119_CR130 publication-title: IEEE Trans Knowl Data Eng doi: 10.1109/TKDE.2022.3220219 – ident: 2119_CR1 – ident: 2119_CR80 doi: 10.1109/CVPR52688.2022.00991 – ident: 2119_CR74 doi: 10.1109/CVPR52688.2022.00993 – ident: 2119_CR172 – volume: 8 start-page: 1084 issue: 2 year: 2020 ident: 2119_CR50 publication-title: IEEE Trans Netw Sci Eng doi: 10.1109/TNSE.2020.2996612 – ident: 2119_CR36 doi: 10.1145/3394486.3403176 – ident: 2119_CR75 – volume: 265 start-page: 110347 year: 2023 ident: 2119_CR58 publication-title: Knowl-Based Syst doi: 10.1016/j.knosys.2023.110347 – year: 2023 ident: 2119_CR72 publication-title: IEEE Trans Parallel Distrib Syst doi: 10.1109/TPDS.2023.3289444 – volume: 35 start-page: 7852 year: 2022 ident: 2119_CR87 publication-title: Adv Neural Inf Process Syst – ident: 2119_CR108 – ident: 2119_CR125 – ident: 2119_CR34 – ident: 2119_CR177 – ident: 2119_CR86 – ident: 2119_CR171 doi: 10.24963/ijcai.2022/306 – ident: 2119_CR40 – ident: 2119_CR28 – volume: 35 start-page: 17871 year: 2022 ident: 2119_CR77 publication-title: Adv Neural Inf Process Syst – volume: 33 start-page: 596 year: 2020 ident: 2119_CR129 publication-title: Adv Neural Inf Process Syst – ident: 2119_CR168 doi: 10.24963/ijcai.2021/217 – ident: 2119_CR114 – volume: 34 start-page: 909 issue: 3 year: 2022 ident: 2119_CR183 publication-title: IEEE Trans Parallel Distrib Syst doi: 10.1109/TPDS.2022.3230938 – ident: 2119_CR93 – ident: 2119_CR21 doi: 10.1609/aaai.v35i9.16960 – ident: 2119_CR166 – ident: 2119_CR81 doi: 10.1109/CVPR52729.2023.01563 – ident: 2119_CR84 doi: 10.1007/978-3-030-60636-7_1 – ident: 2119_CR205 doi: 10.1007/978-3-030-63076-8_17 – volume: 36 start-page: 6865 year: 2022 ident: 2119_CR61 publication-title: Proc AAAI Conf Artif Intell – ident: 2119_CR197 doi: 10.1109/IJCNN55064.2022.9892624 – ident: 2119_CR155 – volume: 252 start-page: 109384 year: 2022 ident: 2119_CR53 publication-title: Knowl-Based Syst doi: 10.1016/j.knosys.2022.109384 – ident: 2119_CR195 doi: 10.1145/3534678.3539308 – volume: 35 start-page: 4224 year: 2021 ident: 2119_CR196 publication-title: Proc AAAI Conf Artif Intel – ident: 2119_CR138 doi: 10.1017/9781108571401 – ident: 2119_CR184 – ident: 2119_CR92 – ident: 2119_CR16 – volume: 24 start-page: 1 year: 2023 ident: 2119_CR64 publication-title: J Mach Learn Res – ident: 2119_CR8 – ident: 2119_CR161 – ident: 2119_CR142 – volume: 37 start-page: 7494 year: 2023 ident: 2119_CR45 publication-title: Proc AAAI Conf Artif Intell – ident: 2119_CR148 doi: 10.1109/TKDE.2022.3172903 – volume: 3 start-page: 9 year: 1988 ident: 2119_CR137 publication-title: Mach Learn – volume: 90 start-page: 102976 year: 2023 ident: 2119_CR203 publication-title: Med Image Anal doi: 10.1016/j.media.2023.102976 – ident: 2119_CR150 – ident: 2119_CR189 – volume: 37 start-page: 10473 year: 2023 ident: 2119_CR43 publication-title: Proc AAAI Conf Artif Intell – volume: 18 start-page: 4788 issue: 7 year: 2021 ident: 2119_CR200 publication-title: IEEE Trans Industr Inf doi: 10.1109/TII.2021.3113708 – ident: 2119_CR3 – ident: 2119_CR13 doi: 10.1109/IJCNN48605.2020.9207469 – ident: 2119_CR176 – volume: 2023 start-page: 1314 year: 2023 ident: 2119_CR70 publication-title: Proc ACM Web Conf – ident: 2119_CR27 – ident: 2119_CR97 doi: 10.1109/ICCV51070.2023.01509 – ident: 2119_CR153 – ident: 2119_CR144 – ident: 2119_CR121 – ident: 2119_CR167 doi: 10.24963/ijcai.2021/202 – ident: 2119_CR182 – ident: 2119_CR104 – ident: 2119_CR11 doi: 10.1109/IJCNN.2019.8852464 – ident: 2119_CR127 – ident: 2119_CR151 doi: 10.1007/978-3-030-72188-6_6 – volume: 35 start-page: 6885 issue: 8 year: 2021 ident: 2119_CR162 publication-title: Proc AAAI Conf Artif Intell doi: 10.1609/aaai.v35i8.16849 – ident: 2119_CR82 – ident: 2119_CR76 – volume: 18 start-page: 1 issue: 1 year: 2023 ident: 2119_CR105 publication-title: ACM Trans Knowl Discov Data doi: 10.1145/3604939 – ident: 2119_CR110 – volume: 24 start-page: 1181 issue: 8 year: 2023 ident: 2119_CR94 publication-title: Front Inf Technol Electron Eng doi: 10.1631/FITEE.2200268 – volume: 13 start-page: 1 issue: 4 year: 2022 ident: 2119_CR198 publication-title: ACM Trans Intell Syst Technol (TIST) – volume: 39 start-page: 154 issue: 1 year: 2020 ident: 2119_CR22 publication-title: IEEE J Sel Areas Commun doi: 10.1109/JSAC.2020.3036946 – volume: 13 start-page: 1 issue: 4 year: 2022 ident: 2119_CR83 publication-title: ACM Trans Intell Syst Technol (TIST) doi: 10.1145/3510031 – ident: 2119_CR128 doi: 10.1109/MASS52906.2021.00031 – volume: 36 start-page: 9171 issue: 8 year: 2022 ident: 2119_CR160 publication-title: Proc AAAI Conf Artif Intell doi: 10.1609/aaai.v36i8.20903 – volume: 35 start-page: 1637 issue: 2 year: 2021 ident: 2119_CR149 publication-title: IEEE Trans Knowl Data Eng – ident: 2119_CR187 – ident: 2119_CR44 – ident: 2119_CR38 – ident: 2119_CR31 doi: 10.1109/ICCV51070.2023.01559 – ident: 2119_CR103 – year: 2023 ident: 2119_CR79 publication-title: IEEE Trans Neural Netw Learn Syst doi: 10.1109/TNNLS.2022.3233093 – ident: 2119_CR47 doi: 10.1145/3523227.3546771 – ident: 2119_CR111 doi: 10.1007/978-3-030-63076-8_1 – volume: 22 start-page: 2031 issue: 3 year: 2020 ident: 2119_CR112 publication-title: IEEE Commun Surv Tutor doi: 10.1109/COMST.2020.2986024 – ident: 2119_CR132 – ident: 2119_CR193 – volume: 8 start-page: 279 year: 1992 ident: 2119_CR136 publication-title: Mach Learn – ident: 2119_CR46 doi: 10.1007/978-3-030-63076-8_14 – ident: 2119_CR24 doi: 10.1109/GLOBECOM48099.2022.10000892 – volume: 7 start-page: 639 issue: 2 year: 2020 ident: 2119_CR152 publication-title: Complex Intell Syst doi: 10.1007/s40747-020-00247-z – volume: 35 start-page: 10807 issue: 12 year: 2021 ident: 2119_CR158 publication-title: Proc AAAI Conf Artif Intell doi: 10.1609/aaai.v35i12.17291 – ident: 2119_CR188 – ident: 2119_CR49 – ident: 2119_CR165 – volume: 34 start-page: 15434 year: 2021 ident: 2119_CR59 publication-title: Adv Neural Inf Process Syst – ident: 2119_CR17 doi: 10.1109/ISPDC52870.2021.9521631 – ident: 2119_CR26 – volume: 36 start-page: 8432 issue: 8 year: 2022 ident: 2119_CR33 publication-title: Proc AAAI Conf Artif Intell doi: 10.1609/aaai.v36i8.20819 – year: 2023 ident: 2119_CR54 publication-title: IEEE Trans Intell Transp Syst doi: 10.1109/TITS.2023.3286439 – ident: 2119_CR99 – volume: 35 start-page: 8688 issue: 10 year: 2021 ident: 2119_CR156 publication-title: Proc AAAI Conf Artif Intell doi: 10.1609/aaai.v35i10.17053 – ident: 2119_CR115 – ident: 2119_CR15 – ident: 2119_CR88 – volume: 35 start-page: 38411 year: 2022 ident: 2119_CR190 publication-title: Adv Neural Inf Process Syst – volume: 10 start-page: 318 issue: 1 year: 2022 ident: 2119_CR180 publication-title: IEEE Internet Things J doi: 10.1109/JIOT.2022.3201231 – ident: 2119_CR143 – ident: 2119_CR109 – ident: 2119_CR126
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Snippet	Federated learning is a new learning paradigm that decouples data collection and model training via multi-party computation and model aggregation. As a...
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SubjectTerms	Adaptive algorithms Algorithms Artificial Intelligence Bayesian analysis Communication Complex Systems Computational Intelligence Control Engineering Federated learning Informatics Machine learning Mechatronics Original Article Paradigms Pattern Recognition Privacy Regularization Regularization methods Robotics State-of-the-art reviews Systems Biology Taxonomy Trends Unsupervised learning
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Title	Emerging trends in federated learning: from model fusion to federated X learning
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