FedNDA: Enhancing Federated Learning with Noisy Client Detection and Robust Aggregation by Tuan Dung Kieu, Charles Fonbonne, Trung-Kien Tran, Thi-Lan Le, Hai Vu, Huu-Thanh Nguyen, Thanh-Hai Tran

“…To address this problem, this paper introduces a Federated learning framework with Noisy client Detection and robust Aggregation, FedNDA. In the first stage, FedNDA detects noisy clients by analyzing the distribution of their local losses. …”
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Enhancing Byzantine robustness of federated learning via tripartite adaptive authentication by Xiaomeng Li, Yanjun Li, Hui Wan, Cong Wang

“…Through these dedicated settings, BRFLATA can authenticate each client, detect potential Byzantine clients and link attackers, and mitigate their impact on the global model’s performance by adjusting the clients’ weights during global model aggregation. …”
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