Connecting Low-Loss Subspace for Personalized Federated Learning

TL;DR

This paper introduces SuPerFed, a novel personalized federated learning method that leverages low-loss subspace connections between models to enhance personalization and robustness across diverse client data.

Contribution

It proposes a new approach that explicitly connects local and federated models in weight space, improving personalization without extra local updates or partial parameter exchange.

Findings

Achieves consistent improvements in personalization performance.

Demonstrates robustness in realistic federated learning scenarios.

Validates effectiveness across multiple benchmark datasets.

Abstract

Due to the curse of statistical heterogeneity across clients, adopting a personalized federated learning method has become an essential choice for the successful deployment of federated learning-based services. Among diverse branches of personalization techniques, a model mixture-based personalization method is preferred as each client has their own personalized model as a result of federated learning. It usually requires a local model and a federated model, but this approach is either limited to partial parameter exchange or requires additional local updates, each of which is helpless to novel clients and burdensome to the client's computational capacity. As the existence of a connected subspace containing diverse low-loss solutions between two or more independent deep networks has been discovered, we combined this interesting property with the model mixture-based personalized federated learning method for improved performance of personalization. We proposed SuPerFed, a personalized federated learning method that induces an explicit connection between the optima of the local and the federated model in weight space for boosting each other. Through extensive experiments on several benchmark datasets, we demonstrated that our method achieves consistent gains in both personalization performance and robustness to problematic scenarios possible in realistic services.