An FL scheme combining client grouping, gradient splitting, performance-based malicious detection, and threshold encryption aims to resist gradient inversion and poisoning attacks, claiming over 95% malicious-client localization accuracy.
Data and model poisoning backdoor attacks on wireless federated learning, and the defense mechanisms: A comprehensive survey,
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SMTFL: Secure Model Training to Untrusted Participants in Federated Learning
An FL scheme combining client grouping, gradient splitting, performance-based malicious detection, and threshold encryption aims to resist gradient inversion and poisoning attacks, claiming over 95% malicious-client localization accuracy.