Your Neighbors Know: Leveraging Local Neighborhoods for Backdoor Detection in Decentralized Learning

TL;DR

This paper introduces Argus, a decentralized backdoor detection framework that leverages local neighborhood analysis and collaborative sharing to identify malicious model updates without a central coordinator.

Contribution

Argus is the first DL-specific backdoor detection method with theoretical guarantees, utilizing local analysis and neighbor collaboration to effectively detect backdoors.

Findings

Argus reduces attack success rates by up to 90 points.

It maintains model utility within 5% of an oracle.

Effectiveness increases with data heterogeneity.

Abstract

Decentralized learning (DL) is an emerging machine learning paradigm where nodes collaboratively train models without a central server. However, the collaborative nature of DL makes it vulnerable to backdoor attacks, where a model is taught to behave normally on standard inputs while executing hidden, malicious actions when encountering data with specific triggers. Backdoor attacks in DL remain understudied and existing defenses often overlook DL constraints. We introduce Argus, a novel backdoor detection framework native to DL that requires neither a central coordinator nor prior knowledge of the trigger. In Argus, honest nodes locally analyze received model updates to identify potential backdoor triggers. Nodes then collectively share their triggers with their neighbors and use a structural similarity metric to separate true backdoors from false alarms induced by data heterogeneity. A key insight is that false positive triggers exhibit inconsistencies across participants while true positive ones show consistent patterns. Model updates that fail this collaborative test are rejected, and persistently malicious senders are eventually evicted. We provide the first theoretical convergence guarantees for a DL-specific backdoor detection mechanism, showing that filtering out suspicious model updates with high probability preserves a convergence rate comparable to standard DL. We implement and evaluate Argus on three standard datasets and against three state-of-the-art baselines. Across settings, Argus reduces attack success rates by up to 90 points compared to no defense, while preserving model utility within 5 percentage points of an omniscient oracle. Furthermore, the effectiveness of Argus compared to baselines improves as data heterogeneity increases.