FedEDAuth filters malicious clients in federated learning for counterfeit IC detection by analyzing embedding distributions from a golden reference, achieving 100% detection of poisoned clients and 94.17% model accuracy in tests with 50 participants.
Challenges and approaches for mitigating byzantine attacks in federated learning,
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FedEDAuth -- Federated Embedding Distribution Authentication for Counterfeit IC Detection
FedEDAuth filters malicious clients in federated learning for counterfeit IC detection by analyzing embedding distributions from a golden reference, achieving 100% detection of poisoned clients and 94.17% model accuracy in tests with 50 participants.