Rising from Ashes: Generalized Federated Learning via Dynamic Parameter Reset

Although Federated Learning (FL) is promising for privacy-preserving collaborative model training, it suffers from low inference performance due to heterogeneous client data. Due to heterogeneous data across clients, FL training easily learns client-specific overfitting features. Existing FL methods adopt coarse-grained averaging, which can easily cause the global model to get stuck in lo-cal optima, leading to poor generalization. Specifically, this paper presents a novel FL framework, FedPhoenix, to address this issue. It stochastically re-sets partial parameters in each round to destroy some features of the global model, guiding FL training to learn multiple generalized features for inference rather than specific overfitting features. Experimental results on various well-known datasets demonstrate that compared to SOTA FL methods, FedPhoenix can achieve up to 20.73% higher accuracy. The implementation is publicly available at https://github.com/UniString/FedPhoenix .