Few-for-Many Personalized Federated Learning

This paper addresses scalability in Personalized Federated Learning (PFL) for heterogeneous data distributions by reformulating PFL as a "few-for-many" optimization problem. It maintains a small number of shared server models (K << M clients) to collectively serve all clients, rather than M distinct models. The proposed algorithm, FedFew, automatically discovers optimal model diversity through efficient gradient-based updates, achieving near-optimal personalization and outperforming state-of-the-art approaches with as few as 3 models on vision, NLP, and medical imaging datasets.