Personalized Federated Learning with Parameter Propagation.

With decentralized data collected from diverse clients, a personalized federated learning paradigm has been proposed for training machine learning models without exchanging raw data from local clients. We dive into personalized federated learning from the perspective of privacy-preserving transfer learning, and identify the limitations of previous personalized federated learning algorithms. First, previous works suffer from negative knowledge transferability for some clients, when focusing more on the overall performance of all clients. Second, high communication costs are required to explicitly learn statistical task relatedness among clients. Third, it is computationally expensive to generalize the learned knowledge from experienced clients to new clients. To solve these problems, in this paper, we propose a novel federated parameter propagation (FEDORA) framework for personalized federated learning. Specifically, we reformulate the standard personalized federated learning as a privacy-preserving transfer learning problem, with the goal of improving the generalization performance for every client. The crucial idea behind FEDORA is to learn how to transfer and whether to transfer simultaneously, including (1) adaptive parameter propagation: one client is enforced to adaptively propagate its parameters to others based on their task relatedness (e.g., explicitly measured by distribution similarity), and (2) selective regularization: each client would regularize its local personalized model with received parameters, only when those parameters are positively correlated with the generalization performance of its local model. The experiments on a variety of federated learning benchmarks demonstrate the effectiveness of the proposed FEDORA framework over state-of-the-art personalized federated learning baselines.