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cs.LG2026
FedIDM: Achieving Fast and Stable Convergence in Byzantine Federated Learning through Iterative Distribution Matching
He Yang, Dongyi Lv, Wei Xi +3
Most existing Byzantine-robust federated learning (FL) methods suffer from slow and unstable convergence. Moreover, when handling a substantial proportion of colluded malicious cli…
cs.LG2026
PoiCGAN: A Targeted Poisoning Based on Feature-Label Joint Perturbation in Federated Learning
Tao Liu, Jiguang Lv, Dapeng Man +7
Federated Learning (FL), as a popular distributed learning paradigm, has shown outstanding performance in improving computational efficiency and protecting data privacy, and is wid…