Changli Zhou

FedGain: Toward Negative-Gain-Free Client Collaboration in Federated Learning

Data heterogeneity is a fundamental challenge in Federated Learning (FL), where induced model drift often results in "negative gains" for global models on data-abundant clients, with performance falling below that of local training. To address this issue, we propose FedGain, a novel framework that o…