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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.LG2024
Poisoning with A Pill: Circumventing Detection in Federated Learning
Hanxi Guo, Hao Wang, Tao Song +4
Without direct access to the client's data, federated learning (FL) is well-known for its unique strength in data privacy protection among existing distributed machine learning tec…