Personalized Federated Learning with Contextualized Generalization

TL;DR

This paper introduces a novel personalized federated learning framework called CGPFL that leverages contextualized generalization to improve local model fitting, convergence speed, and the trade-off between personalization and generalization, supported by theoretical guarantees and superior experimental results.

Contribution

The paper proposes the concept of contextualized generalization and designs CGPFL, a framework that enhances personalized federated learning with better convergence and trade-off control.

Findings

Achieves faster convergence with $ ext{O}(\sqrt{K})$ speedup.

Surpasses state-of-the-art methods in test accuracy.

Provides theoretical guarantees on convergence and generalization trade-off.

Abstract

The prevalent personalized federated learning (PFL) usually pursues a trade-off between personalization and generalization by maintaining a shared global model to guide the training process of local models. However, the sole global model may easily transfer deviated context knowledge to some local models when multiple latent contexts exist across the local datasets. In this paper, we propose a novel concept called contextualized generalization (CG) to provide each client with fine-grained context knowledge that can better fit the local data distributions and facilitate faster model convergence, based on which we properly design a framework of PFL, dubbed CGPFL. We conduct detailed theoretical analysis, in which the convergence guarantee is presented and $\mathcal{O}(\sqrt{K})$ speedup over most existing methods is granted. To quantitatively study the generalization-personalization trade-off, we introduce the 'generalization error' measure and prove that the proposed CGPFL can achieve a better trade-off than existing solutions. Moreover, our theoretical analysis further inspires a heuristic algorithm to find a near-optimal trade-off in CGPFL. Experimental results on multiple real-world datasets show that our approach surpasses the state-of-the-art methods on test accuracy by a significant margin.