For Thirty Pieces of Silver: Overcoming Non-IID via ZKP Sample-Level Auditing and Forcing Attackers into Traitors in Federated Learning↗ (원문, 새 창)

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As a privacy-preserving distributed learning paradigm, federated learning faces severe security threats, especially data poisoning, gradient tampering, distributed attacks, and adaptive attacks. Existing gradient-based detection methods encounter three major bottlenecks in practical applications. First, they cannot distinguish between benign and malicious gradients under distributed attacks in non-IID scenarios. Second, defenders lack prior knowledge of trigger patterns. Third, attackers can lau