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
Federated learning (FL) is a nascent distributed learning paradigm to train a shared global model without violating users' privacy. FL has been shown to be vulnerable to various Byzantine attacks, where malicious participants could independently or ...
Federated learning, as a distributed learning that conducts the training on the local devices without accessing to the training data, is vulnerable to Byzantine poisoning adversarial attacks. We argue that the federated learning model ...
Federated Learning (FL) is susceptible to poisoning attacks, wherein compromised clients manipulate the global model by modifying local datasets or sending manipulated model updates. Experienced defenders can readily detect and mitigate the poisoning ...
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