Fine-grained Distributed Backdoor Attacks in Federated Learning
Federated learning, as a privacy-preserving distributed machine learning paradigm, faces significant threats from backdoor attacks. Compared to centralized attacks, distributed backdoor attacks are more harmful but require more poisoned samples to compensate for the loss of trigger strength due to decomposition. Fixed...
Jian Wang, Hong Shen, Wei Ke et al.
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