UA-PDFL: A Personalized Approach for Decentralized Federated Learning

TL;DR

UA-PDFL introduces a personalized decentralized federated learning framework that adaptively adjusts personalization layers to effectively handle non-IID data distributions, improving performance without relying on a central server.

Contribution

The paper proposes UA-PDFL, a novel personalized DFL framework that uses unit representation to adaptively tune personalization layers, addressing non-IID data challenges.

Findings

UA-PDFL outperforms existing DFL methods in non-IID scenarios.

Client-wise dropout enhances learning robustness.

Layer-wise personalization improves model accuracy.

Abstract

Federated learning (FL) is a privacy preserving machine learning paradigm designed to collaboratively learn a global model without data leakage. Specifically, in a typical FL system, the central server solely functions as an coordinator to iteratively aggregate the collected local models trained by each client, potentially introducing single-point transmission bottleneck and security threats. To mitigate this issue, decentralized federated learning (DFL) has been proposed, where all participating clients engage in peer-to-peer communication without a central server. Nonetheless, DFL still suffers from training degradation as FL does due to the non-independent and identically distributed (non-IID) nature of client data. And incorporating personalization layers into DFL may be the most effective solutions to alleviate the side effects caused by non-IID data. Therefore, in this paper, we propose a novel unit representation aided personalized decentralized federated learning framework, named UA-PDFL, to deal with the non-IID challenge in DFL. By adaptively adjusting the level of personalization layers through the guidance of the unit representation, UA-PDFL is able to address the varying degrees of data skew. Based on this scheme, client-wise dropout and layer-wise personalization are proposed to further enhance the learning performance of DFL. Extensive experiments empirically prove the effectiveness of our proposed method.