Don't Reach for the Stars: Rethinking Topology for Resilient Federated Learning

TL;DR

This paper introduces LIGHTYEAR, a decentralized peer-to-peer federated learning framework that enhances robustness and personalization by using local agreement scores for update selection, outperforming traditional centralized methods especially in challenging environments.

Contribution

The paper proposes a novel P2P FL framework using local agreement scores for personalized update aggregation, improving robustness and performance over existing methods.

Findings

Outperforms centralized and existing P2P FL methods on five datasets.

Enhances robustness under adversarial and heterogeneous conditions.

Provides personalized and stable model updates.

Abstract

Federated learning (FL) enables collaborative model training across distributed clients while preserving data privacy by keeping data local. Traditional FL approaches rely on a centralized, star-shaped topology, where a central server aggregates model updates from clients. However, this architecture introduces several limitations, including a single point of failure, limited personalization, and poor robustness to distribution shifts or vulnerability to malfunctioning clients. Moreover, update selection in centralized FL often relies on low-level parameter differences, which can be unreliable when client data is not independent and identically distributed, and offer clients little control. In this work, we propose a decentralized, peer-to-peer (P2P) FL framework. It leverages the flexibility of the P2P topology to enable each client to identify and aggregate a personalized set of trustworthy and beneficial updates.This framework is the Local Inference Guided Aggregation for Heterogeneous Training Environments to Yield Enhancement Through Agreement and Regularization (LIGHTYEAR). Central to our method is an agreement score, computed on a local validation set, which quantifies the semantic alignment of incoming updates in the function space with respect to the clients reference model. Each client uses this score to select a tailored subset of updates and performs aggregation with a regularization term that further stabilizes the training. Our empirical evaluation across five datasets shows that the proposed approach consistently outperforms both, centralized baselines and existing P2P methods in terms of client-level performance, particularly under adversarial and heterogeneous conditions.