WarmFed: Federated Learning With Warm-Start for Globalization and Personalization via Personalized Diffusion Models

Although federated learning stands as a prominent distributed learning paradigm across multiple clients, it has grappled with a dilemma: the choice between developing a singular global model to promote global generalization or nurturing personalized models to accommodate personalization. In this article, a federated learning with warm-start (WarmFed) is proposed to obtain both robust global and personalized models. First, a client knowledge-driven initialization approach named warm-start is introduced, which provides resilient global information to assist subsequent training. Then, a dynamic self-distillation (DSD) strategy is proposed to obtain more resilient personalized models and a model fine-tuning (MFT) mechanism based on synthetic data is designed to improve performance of the global model on the server side. The collaborative deployment of DSD and MFT exploits potential to achieve both generalization and personalization under warm-start. Comprehensive experiments underscore the superiority of our approach across both global and personalized models under domain shift and label skew.