Cited By
View all- Bowen DHaiquan WYuxuan LZhao JMa YRunhe H(2024)Fair and Robust Federated Learning via Decentralized and Adaptive Aggregation based on BlockchainACM Transactions on Sensor Networks10.1145/3673656Online publication date: 17-Jun-2024
Differential privacy (DP) is considered as an effective privacy-preserving method in federation learning to defend against privacy attacks. However, recent studies have shown that it can be exploited to perform security attacks (e.g., false data ...
Robustness of federated learning has become one of the major concerns since some Byzantine adversaries, who may upload false data owning to unreliable communication channels, corrupted hardware or even malicious attacks, might be ...
Privacy-preserving federated learning (PPFL) enables collaborative model training across multiple parties while protecting the privacy of sensitive data. However, PPFL is vulnerable to poisoning attacks, as the indistinguishability of ciphertext ...
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