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

arXiv: https://arxiv.org/abs/2605.19969 · HTML: https://mlsanity.com/paper/2605.19969/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 34 of its 62 references that are in the map.
- First posted on arXiv 19 May 2026.

## Builds on (most cited first, 30 of 34)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [LEAF: A Benchmark for Federated Settings](https://mlsanity.com/paper/1812.01097/) (2018)
- [How To Backdoor Federated Learning](https://mlsanity.com/paper/1807.00459/) (2018)
- [Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates](https://mlsanity.com/paper/1803.01498/) (2018)
- [Can Decentralized Algorithms Outperform Centralized Algorithms? A Case Study for Decentralized Parallel Stochastic Gradient Descent](https://mlsanity.com/paper/1705.09056/) (2017)
- [A Unified Theory of Decentralized SGD with Changing Topology and Local Updates](https://mlsanity.com/paper/2003.10422/) (2020)
- [Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and Challenges](https://mlsanity.com/paper/2211.08413/) (2022)
- [Robust Anomaly Detection and Backdoor Attack Detection Via Differential Privacy](https://mlsanity.com/paper/1911.07116/) (2019)
- [CRFL: Certifiably Robust Federated Learning against Backdoor Attacks](https://mlsanity.com/paper/2106.08283/) (2021)
- [DeepSight: Mitigating Backdoor Attacks in Federated Learning Through Deep Model Inspection](https://mlsanity.com/paper/2201.00763/) (2022)
- [Collaborative Learning in the Jungle (Decentralized, Byzantine, Heterogeneous, Asynchronous and Nonconvex Learning)](https://mlsanity.com/paper/2008.00742/) (2020)
- [BaFFLe: Backdoor detection via Feedback-based Federated Learning](https://mlsanity.com/paper/2011.02167/) (2020)
- [BRIDGE: Byzantine-resilient Decentralized Gradient Descent](https://mlsanity.com/paper/1908.08098/) (2019)
- [FL-WBC: Enhancing Robustness against Model Poisoning Attacks in Federated Learning from a Client Perspective](https://mlsanity.com/paper/2110.13864/) (2021)
- [Muffliato: Peer-to-Peer Privacy Amplification for Decentralized Optimization and Averaging](https://mlsanity.com/paper/2206.05091/) (2022)
- [CrowdGuard: Federated Backdoor Detection in Federated Learning](https://mlsanity.com/paper/2210.07714/) (2022)
- [Epidemic Learning: Boosting Decentralized Learning with Randomized Communication](https://mlsanity.com/paper/2310.01972/) (2023)
- [Decentralized Learning Made Easy with DecentralizePy](https://mlsanity.com/paper/2304.08322/) (2023)
- [BackdoorIndicator: Leveraging OOD Data for Proactive Backdoor Detection in Federated Learning](https://mlsanity.com/paper/2405.20862/) (2024)
- [Detecting Backdoor Attacks in Federated Learning via Direction Alignment Inspection](https://mlsanity.com/paper/2503.07978/) (2025)
- [Global Update Tracking: A Decentralized Learning Algorithm for Heterogeneous Data](https://mlsanity.com/paper/2305.04792/) (2023)
- [Unified Analysis of Decentralized Gradient Descent: a Contraction Mapping Framework](https://mlsanity.com/paper/2503.14353/) (2025)
- [Backdoor Attacks in Peer-to-Peer Federated Learning](https://mlsanity.com/paper/2301.09732/) (2023)
- [Noiseless Privacy-Preserving Decentralized Learning](https://mlsanity.com/paper/2404.09536/) (2024)
- [Data Free Backdoor Attacks](https://mlsanity.com/paper/2412.06219/) (2024)
- [DeTrigger: A Gradient-Centric Approach to Backdoor Attack Mitigation in Federated Learning](https://mlsanity.com/paper/2411.12220/) (2024)
- [FedGrad: Mitigating Backdoor Attacks in Federated Learning Through Local Ultimate Gradients Inspection](https://mlsanity.com/paper/2305.00328/) (2023)
- [Low-Cost Privacy-Preserving Decentralized Learning](https://mlsanity.com/paper/2403.11795/) (2024)
- [Systematizing Decentralization and Privacy: Lessons from 15 Years of Research and Deployments](https://mlsanity.com/paper/1704.08065/) (2017)
- [Mitigating Backdoors in Federated Learning with FLD](https://mlsanity.com/paper/2303.00302/) (2023)

## Built on by (most cited first, 1 of 1)

- [Backdoor Mitigation in Decentralized LLM Fine-Tuning](https://mlsanity.com/paper/2609.37367/) (2026)

## Abstract (arXiv)

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.