2 papers
cs.LG2025
FedSSG: Expectation-Gated and History-Aware Drift Alignment for Federated Learning
Zhanting Zhou, Jinshan Lai, Fengchun Zhang +2
Non-IID data and partial participation induce client drift and inconsistent local optima in federated learning, causing unstable convergence and accuracy loss. We present FedSSG, a…
cs.LG2024
How to Defend Against Large-scale Model Poisoning Attacks in Federated Learning: A Vertical Solution
Jinbo Wang, Ruijin Wang, Fengli Zhang
Federated learning (FL) is vulnerable to model poisoning attacks due to its distributed nature. The current defenses start from all user gradients (model updates) in each communica…