FedALA: Adaptive Local Aggregation for Personalized Federated Learning


  • Jianqing Zhang Shanghai Jiao Tong University
  • Yang Hua Queen's University Belfast
  • Hao Wang Louisiana State University
  • Tao Song Shanghai Jiao Tong University
  • Zhengui Xue Shanghai Jiao Tong University
  • Ruhui Ma Shanghai Jiao Tong University
  • Haibing Guan Shanghai Jiao Tong University




ML: Distributed Machine Learning & Federated Learning


A key challenge in federated learning (FL) is the statistical heterogeneity that impairs the generalization of the global model on each client. To address this, we propose a method Federated learning with Adaptive Local Aggregation (FedALA) by capturing the desired information in the global model for client models in personalized FL. The key component of FedALA is an Adaptive Local Aggregation (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.




How to Cite

Zhang, J., Hua, Y., Wang, H., Song, T., Xue, Z., Ma, R., & Guan, H. (2023). FedALA: Adaptive Local Aggregation for Personalized Federated Learning. Proceedings of the AAAI Conference on Artificial Intelligence, 37(9), 11237-11244. https://doi.org/10.1609/aaai.v37i9.26330



AAAI Technical Track on Machine Learning IV