DisPFL: Towards Communication-Efficient Personalized Federated Learning via Decentralized Sparse Training

TL;DR

DisPFL introduces a decentralized, communication-efficient federated learning framework that uses sparse training and personalized masks, reducing communication costs and improving accuracy in heterogeneous client environments.

Contribution

The paper proposes DisPFL, a novel decentralized personalized federated learning method employing sparse training and masks, enhancing communication efficiency and adaptability.

Findings

Significantly reduces communication bottleneck among clients.

Achieves higher model accuracy with less computation and communication rounds.

Effectively adapts to heterogeneous client environments.

Abstract

Personalized federated learning is proposed to handle the data heterogeneity problem amongst clients by learning dedicated tailored local models for each user. However, existing works are often built in a centralized way, leading to high communication pressure and high vulnerability when a failure or an attack on the central server occurs. In this work, we propose a novel personalized federated learning framework in a decentralized (peer-to-peer) communication protocol named Dis-PFL, which employs personalized sparse masks to customize sparse local models on the edge. To further save the communication and computation cost, we propose a decentralized sparse training technique, which means that each local model in Dis-PFL only maintains a fixed number of active parameters throughout the whole local training and peer-to-peer communication process. Comprehensive experiments demonstrate that Dis-PFL significantly saves the communication bottleneck for the busiest node among all clients and, at the same time, achieves higher model accuracy with less computation cost and communication rounds. Furthermore, we demonstrate that our method can easily adapt to heterogeneous local clients with varying computation complexities and achieves better personalized performances.