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cs.LG2026
ARES: Scalable and Practical Gradient Inversion Attack in Federated Learning through Activation Recovery
Zirui Gong, Leo Yu Zhang, Yanjun Zhang +4
Federated Learning (FL) enables collaborative model training by sharing model updates instead of raw data, aiming to protect user privacy. However, recent studies reveal that these…
cs.LG2025
Performance Guaranteed Poisoning Attacks in Federated Learning: A Sliding Mode Approach
Huazi Pan, Yanjun Zhang, Leo Yu Zhang +3
Manipulation of local training data and local updates, i.e., the poisoning attack, is the main threat arising from the collaborative nature of the federated learning (FL) paradigm.…
cs.LG2025
Not All Edges are Equally Robust: Evaluating the Robustness of Ranking-Based Federated Learning
Zirui Gong, Yanjun Zhang, Leo Yu Zhang +3
Federated Ranking Learning (FRL) is a state-of-the-art FL framework that stands out for its communication efficiency and resilience to poisoning attacks. It diverges from the tradi…