FedCut: A Spectral Analysis Framework for Reliable Detection of Byzantine Colluders

TL;DR

FedCut introduces a spectral analysis framework that effectively detects Byzantine colluders in federated learning, significantly improving model robustness against malicious attacks through community detection techniques.

Contribution

The paper presents a novel spectral analysis approach using community detection to identify Byzantine colluders, enhancing robustness in federated learning systems.

Findings

FedCut achieves 2.1% to 16.5% higher average model performance under attacks.

It outperforms existing methods with 17.6% to 69.5% better worst-case performance.

The framework converges with bounded errors, ensuring reliable detection.

Abstract

This paper proposes a general spectral analysis framework that thwarts a security risk in federated Learning caused by groups of malicious Byzantine attackers or colluders, who conspire to upload vicious model updates to severely debase global model performances. The proposed framework delineates the strong consistency and temporal coherence between Byzantine colluders' model updates from a spectral analysis lens, and, formulates the detection of Byzantine misbehaviours as a community detection problem in weighted graphs. The modified normalized graph cut is then utilized to discern attackers from benign participants. Moreover, the Spectral heuristics is adopted to make the detection robust against various attacks. The proposed Byzantine colluder resilient method, i.e., FedCut, is guaranteed to converge with bounded errors. Extensive experimental results under a variety of settings justify the superiority of FedCut, which demonstrates extremely robust model performance (MP) under various attacks. It was shown that FedCut's averaged MP is 2.1% to 16.5% better than that of the state of the art Byzantine-resilient methods. In terms of the worst-case model performance (MP), FedCut is 17.6% to 69.5% better than these methods.