FL-PLAS defends federated learning against backdoor attacks by aggregating only feature extractors and keeping classifiers client-local, reporting low backdoor accuracy with up to 90% malicious clients.
Defending against backdoors in federated learning with robust learning rate
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FL-PLAS: Federated Learning with Partial Layer Aggregation for Backdoor Defense Against High-Ratio Malicious Clients
FL-PLAS defends federated learning against backdoor attacks by aggregating only feature extractors and keeping classifiers client-local, reporting low backdoor accuracy with up to 90% malicious clients.