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cs.LG2025
FedThief: Harming Others to Benefit Oneself in Self-Centered Federated Learning
Xiangyu Zhang, Mang Ye
In federated learning, participants' uploaded model updates cannot be directly verified, leaving the system vulnerable to malicious attacks. Existing attack strategies have adversa…
cs.LG2023★ 6 cited
Rethinking Client Drift in Federated Learning: A Logit Perspective
Yunlu Yan, Chun-Mei Feng, Mang Ye +5
Federated Learning (FL) enables multiple clients to collaboratively learn in a distributed way, allowing for privacy protection. However, the real-world non-IID data will lead to c…