Federated learning for 6G communications: Challenges, methods, and future directions

• Published in: China communications Vol. 17; no. 9; pp. 105 - 118
• Main Authors: Liu, Yi, Yuan, Xingliang, Xiong, Zehui, Kang, Jiawen, Wang, Xiaofei, Niyato, Dusit
• Format: Journal Article
• Language: English
• Published: China Institute of Communications 01.09.2020; School of Data Science of Technology, Heilongjiang University, Harbin, China%Faculty of Information Technology, Monash University, Australia%Alibaba-NTU Joint Research Institute and also School of Computer Science and Engineering, NTU, Singapore%Energy Research Institute, Nanyang Technological University, Singapore%College of Intelligence and Computing, Tianjin University, Tianjin, China%School of Computer Science and Engineering, NTU, Singapore
• Subjects: 5G mobile communication; 6G communication; 6G mobile communication; Artificial intelligence; Collaborative work; Communication system security; federated learning; Optimization; security and privacy protection; Wireless communication; 6G communication; security and privacy protection; federated learning
• ISSN: 1673-5447
• DOI: 10.23919/JCC.2020.09.009

As the 5G communication networks are being widely deployed worldwide, both industry and academia have started to move beyond 5G and explore 6G communications. It is generally believed that 6G will be established on ubiquitous Artificial Intelligence (AI) to achieve data-driven Machine Learning (ML) solutions in heterogeneous and massive-scale networks. However, traditional ML techniques require centralized data collection and processing by a central server, which is becoming a bottleneck of large-scale implementation in daily life due to significantly increasing privacy concerns. Federated learning, as an emerging distributed AI approach with privacy preservation nature, is particularly attractive for various wireless applications, especially being treated as one of the vital solutions to achieve ubiquitous AI in 6G. In this article, we first introduce the integration of 6G and federated learning and provide potential federated learning applications for 6G. We then describe key technical challenges, the corresponding federated learning methods, and open problems for future research on federated learning in the context of 6G communications.