Learning Shareable Bases for Personalized Federated Image Classification

Personalized federated learning (PFL) aims to leverage the collective wisdom of clients' data while constructing customized models that are tailored to individual client's data distributions. The existing work of PFL mostly aims to personalize for participating clients. In this paper, we focus on a less studied but practically important scenario---generating a personalized model for a new client efficiently. Different from most previous approaches that learn a whole or partial network for each client, we explicitly model the clients' overall meta distribution and embed each client into a low dimension space. We propose FedBasis, a novel PFL algorithm that learns a set of few, shareable basis models, upon which each client only needs to learn the coefficients for combining them into a personalized network. FedBasis is parameter-efficient, robust, and more accurate compared to other competitive PFL baselines, especially in a low data regime, without increasing the inference cost. To demonstrate its applicability, we further present a PFL evaluation protocol for image classification, featuring larger data discrepancies across clients in both the image and label spaces as well as more faithful training and test splits.