DM-PFL: Hitchhiking Generic Federated Learning for Efficient Shift-Robust Personalization

Personalized federated learning collaboratively trains client-specific models, which holds potential for various mobile and IoT applications with heterogeneous data. However, existing solutions are vulnerable to distribution shifts between training and test data, and involve high training workloads on local devices. These two shortcomings hinder the practical usage of personalized federated learning on real-world mobile applications. To overcome these drawbacks, we explore efficient shift-robust personalization for federated learning. The principle is to hitchhike the global model to improve the shift-robustness of personalized models with minimal extra training overhead. To this end, we present DM-PFL, a novel framework that utilizes a dual masking mechanism to train both global and personalized models with weight-level parameter sharing and end-to-end sparse training. Evaluations on various datasets show that our methods not only improve the test accuracy in presence of test-time distribution shifts but also save the communication and computation costs compared to state-of-the-art personalized federated learning schemes.