PRIOR: Personalized Prior for Reactivating the Information Overlooked in Federated Learning

TL;DR

This paper introduces pFedBreD, a novel personalized federated learning framework that injects client-specific prior knowledge into the global model, improving personalization and performance across diverse datasets.

Contribution

The paper proposes a new method, pFedBreD, which decouples personalized prior from local objectives using Bregman divergence, enhancing adaptability and performance in personalized federated learning.

Findings

Achieves state-of-the-art results on 5 datasets

Outperforms existing methods by up to 3.5%

Demonstrates robustness and effectiveness of personalized priors

Abstract

Classical federated learning (FL) enables training machine learning models without sharing data for privacy preservation, but heterogeneous data characteristic degrades the performance of the localized model. Personalized FL (PFL) addresses this by synthesizing personalized models from a global model via training on local data. Such a global model may overlook the specific information that the clients have been sampled. In this paper, we propose a novel scheme to inject personalized prior knowledge into the global model in each client, which attempts to mitigate the introduced incomplete information problem in PFL. At the heart of our proposed approach is a framework, the PFL with Bregman Divergence (pFedBreD), decoupling the personalized prior from the local objective function regularized by Bregman divergence for greater adaptability in personalized scenarios. We also relax the mirror descent (RMD) to extract the prior explicitly to provide optional strategies. Additionally, our pFedBreD is backed up by a convergence analysis. Sufficient experiments demonstrate that our method reaches the state-of-the-art performances on 5 datasets and outperforms other methods by up to 3.5% across 8 benchmarks. Extensive analyses verify the robustness and necessity of proposed designs.