FedIDM: Achieving Fast and Stable Convergence in Byzantine Federated Learning through Iterative Distribution Matching

Researchers propose FedIDM, a Byzantine-robust federated learning method that uses distribution matching to identify malicious clients and stabilize convergence. The approach combines attack-tolerant data generation with contribution-based filtering to maintain model utility while handling colluded adversaries.

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