Towards Federated Learning Models Resistant to Adversarial Attacks

With the popularity of the internet of things (IoT) and crowdsensing, sample data are more detailed and diverse. Users tend to avoid uploading personal data for privacy protection. Federated Learning (FL) provides a new learning paradigm to complete training tasks without compromising user privacy....

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Bibliographic Details
Published inIEEE internet of things journal p. 1
Main Authors Hu, Fei, Zhou, Wuneng, Liao, Kaili, Li, Hongliang, Tong, Dongbing
Format Journal Article
LanguageEnglish
Published IEEE 29.04.2023
Subjects
Aggregates
Analytical models
Attack-resistant
Data heterogeneity
Data models
Data privacy
Federated learning
Internet of Things
Internet of Things (IoT)
Privacy preservation
Servers
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Summary:With the popularity of the internet of things (IoT) and crowdsensing, sample data are more detailed and diverse. Users tend to avoid uploading personal data for privacy protection. Federated Learning (FL) provides a new learning paradigm to complete training tasks without compromising user privacy. To deal with the challenge of malicious client attacks in FL systems, we present a Robust Framework for FL (RFFL) that can iteratively filter out malicious clients before federated aggregation, which results in defense capability against different types and levels of attacks. Then we provide a convergence analysis of RFFL. Since client devices and edges distribute in different environments, which may cause client data heterogeneity, we offer an extension of RFFL (Ext. RFFL) to mitigate the effects of heterogeneity with no loss of defense capacity. Extensive experiments with real-world datasets demonstrate that our frameworks are competitive with benchmark algorithms in defending against various types and rates of attacks.
ISSN:2327-4662
DOI:10.1109/JIOT.2023.3272334