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 coarsegrained averaging, which can easily cause the global model to get stuck in local optima, leading to poor generalization. Specifically, this paper presents a novel FL framework, FedPhoenix, to address this issue. It stochastically resets 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 wellknown datasets demonstrate that compared to SOTAFL methods, FedPhoenix can achieve up to 20.73% higher accuracy. The implementation is publicly available at https://github.com/UniString/FedPhoenix.

Although Federated Learning (FL) is promising in privacy-preserving collaborative model training, it faces low inference performance due to heterogeneous data among clients. Due to heterogeneous data in each client, FL training easily learns the specific overfitting features. Existing FL methods adopt the coarse-grained average aggregation strategy, which causes the global model to easily get stuck in local optima, resulting in low generalization of the global model. Specifically, this paper presents a novel FL framework named FedPhoenix to address this issue, which stochastically resets partial parameters to destroy some features of the global model in each round to guide the 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\% accuracy improvement.