FedALA: Adaptive Local Aggregation for Personalized Federated Learning

A key challenge in federated learning (FL) is the statis- tical heterogeneity that impairs the generalization of the global model on each client. To address this, we propose a method Fed erated learning with A daptive L ocal A ggregation ( FedALA ) by capturing the desired information in the global model for client models in personalized FL. The key compo-nent of FedALA is an A daptive L ocal A ggregation ( ALA ) module, which can adaptively aggregate the downloaded global model and local model towards the local objective on each client to initialize the local model before training in each iteration. To evaluate the effectiveness of FedALA, we conduct extensive experiments with five benchmark datasets in computer vision and natural language processing domains. FedALA outperforms eleven state-of-the-art baselines by up to 3.27% in test accuracy. Furthermore, we also apply ALA module to other federated learning methods and achieve up to 24.19% improvement in test accuracy. Code is available at https://github.com/TsingZ0/FedALA.