# Byzantine-Robust Decentralized Federated Learning

arXiv: https://arxiv.org/abs/2406.10416 · HTML: https://mlsanity.com/paper/2406.10416/

## Key facts

- Cited by 48 papers in the map (data through 14 Oct 2026).
- Builds on 38 of its 58 references that are in the map.
- First posted on arXiv 14 Jun 2024.

## Builds on (most cited first, 30 of 38)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [Deep Learning Face Attributes in the Wild](https://mlsanity.com/paper/1411.7766/) (2014)
- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain](https://mlsanity.com/paper/1708.06733/) (2017)
- [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)
- [The Future of Digital Health with Federated Learning](https://mlsanity.com/paper/2003.08119/) (2020)
- [Can Decentralized Algorithms Outperform Centralized Algorithms? A Case Study for Decentralized Parallel Stochastic Gradient Descent](https://mlsanity.com/paper/1705.09056/) (2017)
- [Local Model Poisoning Attacks to Byzantine-Robust Federated Learning](https://mlsanity.com/paper/1911.11815/) (2019)
- [FLTrust: Byzantine-robust Federated Learning via Trust Bootstrapping](https://mlsanity.com/paper/2012.13995/) (2020)
- [The Hidden Vulnerability of Distributed Learning in Byzantium](https://mlsanity.com/paper/1802.07927/) (2018)
- [A Little Is Enough: Circumventing Defenses For Distributed Learning](https://mlsanity.com/paper/1902.06156/) (2019)
- [Data Poisoning Attacks Against Federated Learning Systems](https://mlsanity.com/paper/2007.08432/) (2020)
- [Towards Poisoning of Deep Learning Algorithms with Back-gradient Optimization](https://mlsanity.com/paper/1708.08689/) (2017)
- [Byzantine-Robust Learning on Heterogeneous Datasets via Bucketing](https://mlsanity.com/paper/2006.09365/) (2020)
- [FLDetector: Defending Federated Learning Against Model Poisoning Attacks via Detecting Malicious Clients](https://mlsanity.com/paper/2207.09209/) (2022)
- [Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Production Federated Learning](https://mlsanity.com/paper/2108.10241/) (2021)
- [Robbing the Fed: Directly Obtaining Private Data in Federated Learning with Modified Models](https://mlsanity.com/paper/2110.13057/) (2021)
- [CRFL: Certifiably Robust Federated Learning against Backdoor Attacks](https://mlsanity.com/paper/2106.08283/) (2021)
- [DisPFL: Towards Communication-Efficient Personalized Federated Learning via Decentralized Sparse Training](https://mlsanity.com/paper/2206.00187/) (2022)
- [Byzantine-Robust Federated Machine Learning through Adaptive Model Averaging](https://mlsanity.com/paper/1909.05125/) (2019)
- [MPAF: Model Poisoning Attacks to Federated Learning based on Fake Clients](https://mlsanity.com/paper/2203.08669/) (2022)
- [Eluding Secure Aggregation in Federated Learning via Model Inconsistency](https://mlsanity.com/paper/2111.07380/) (2021)
- [FedRecover: Recovering from Poisoning Attacks in Federated Learning using Historical Information](https://mlsanity.com/paper/2210.10936/) (2022)
- [Decentralized Federated Learning through Proxy Model Sharing](https://mlsanity.com/paper/2111.11343/) (2021)
- [Collaborative Learning in the Jungle (Decentralized, Byzantine, Heterogeneous, Asynchronous and Nonconvex Learning)](https://mlsanity.com/paper/2008.00742/) (2020)
- [Consensus Control for Decentralized Deep Learning](https://mlsanity.com/paper/2102.04828/) (2021)
- [Attack-Resistant Federated Learning with Residual-based Reweighting](https://mlsanity.com/paper/1912.11464/) (2019)

## Built on by (most cited first, 30 of 48)

- [Traceback of Poisoning Attacks to Retrieval-Augmented Generation](https://mlsanity.com/paper/2504.21668/) (2025)
- [Do We Really Need to Design New Byzantine-robust Aggregation Rules?](https://mlsanity.com/paper/2501.17381/) (2025)
- [Provably Robust Federated Reinforcement Learning](https://mlsanity.com/paper/2502.08123/) (2025)
- [Tracing Back the Malicious Clients in Poisoning Attacks to Federated Learning](https://mlsanity.com/paper/2407.07221/) (2024)
- [Byzantine-Robust Federated Learning over Ring-All-Reduce Distributed Computing](https://mlsanity.com/paper/2501.17392/) (2025)
- [Exact and Linear Convergence for Federated Learning under Arbitrary Client Participation is Attainable](https://mlsanity.com/paper/2503.20117/) (2025)
- [Poisoning Attacks and Defenses to Federated Unlearning](https://mlsanity.com/paper/2501.17396/) (2025)
- [From Centralized to Decentralized Federated Learning: Theoretical Insights, Privacy Preservation, and Robustness Challenges](https://mlsanity.com/paper/2503.07505/) (2025)
- [Toward Malicious Clients Detection in Federated Learning](https://mlsanity.com/paper/2505.09110/) (2025)
- [On the Hardness of Decentralized Multi-Agent Policy Evaluation under Byzantine Attacks](https://mlsanity.com/paper/2409.12882/) (2024)
- [On Model Protection in Federated Learning against Eavesdropping Attacks](https://mlsanity.com/paper/2504.02114/) (2025)
- [Beyond Corner Patches: Semantics-Aware Backdoor Attack in Federated Learning](https://mlsanity.com/paper/2603.29328/) (2026)
- [ROSS: RObust decentralized Stochastic learning based on Shapley values](https://mlsanity.com/paper/2411.00365/) (2024)
- [Enhancing Federated Survival Analysis through Peer-Driven Client Reputation in Healthcare](https://mlsanity.com/paper/2505.16190/) (2025)
- [Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning](https://mlsanity.com/paper/2507.00423/) (2025)
- [Screen Before You Fetch: Compressed Byzantine Screening for Decentralized Learning on the Edge-Cloud Continuum](https://mlsanity.com/paper/2510.07922/) (2025)
- [Fairness-Constrained Optimization Attack in Federated Learning](https://mlsanity.com/paper/2510.12143/) (2025)
- [FLARE: Adaptive Multi-Dimensional Reputation for Robust Client Reliability in Federated Learning](https://mlsanity.com/paper/2511.14715/) (2025)
- [Model-Consistent Byzantine-Resilient Decentralized Federated Learning for Collaborative Missions](https://mlsanity.com/paper/2608.22552/) (2026)
- [Gradient Purification: Defense Against Poisoning Attack in Decentralized Federated Learning](https://mlsanity.com/paper/2501.04453/) (2025)
- [GRANITE : a Byzantine-Resilient Dynamic Gossip Learning Framework](https://mlsanity.com/paper/2504.17471/) (2025)
- [Realistic Urban Traffic Generator using Decentralized Federated Learning for the SUMO simulator](https://mlsanity.com/paper/2506.07980/) (2025)
- [Byzantine Outside, Curious Inside: Reconstructing Data Through Malicious Updates](https://mlsanity.com/paper/2506.11413/) (2025)
- [ImprovDML: Improved Trade-off in Private Byzantine-Resilient Distributed Machine Learning](https://mlsanity.com/paper/2506.15181/) (2025)
- [Client Clustering Meets Knowledge Sharing: Enhancing Privacy and Robustness in Personalized Peer-to-Peer Learning](https://mlsanity.com/paper/2506.20413/) (2025)
- [Enhancing Privacy in Decentralized Min-Max Optimization: A Differentially Private Approach](https://mlsanity.com/paper/2508.07505/) (2025)
- [FedThief: Harming Others to Benefit Oneself in Self-Centered Federated Learning](https://mlsanity.com/paper/2509.00540/) (2025)
- [Delta Sum Learning: an approach for fast and global convergence in Gossip Learning](https://mlsanity.com/paper/2512.01549/) (2025)
- [DFedReweighting: A Unified Framework for Objective-Oriented Reweighting in Decentralized Federated Learning](https://mlsanity.com/paper/2512.12022/) (2025)
- [Evidential Trust-Aware Model Personalization in Decentralized Federated Learning for Wearable IoT](https://mlsanity.com/paper/2512.19131/) (2025)

## Abstract (arXiv)

Federated learning (FL) enables multiple clients to collaboratively train machine learning models without revealing their private training data. In conventional FL, the system follows the server-assisted architecture (server-assisted FL), where the training process is coordinated by a central server. However, the server-assisted FL framework suffers from poor scalability due to a communication bottleneck at the server, and trust dependency issues. To address challenges, decentralized federated learning (DFL) architecture has been proposed to allow clients to train models collaboratively in a serverless and peer-to-peer manner. However, due to its fully decentralized nature, DFL is highly vulnerable to poisoning attacks, where malicious clients could manipulate the system by sending carefully-crafted local models to their neighboring clients. To date, only a limited number of Byzantine-robust DFL methods have been proposed, most of which are either communication-inefficient or remain vulnerable to advanced poisoning attacks. In this paper, we propose a new algorithm called BALANCE (Byzantine-robust averaging through local similarity in decentralization) to defend against poisoning attacks in DFL. In BALANCE, each client leverages its own local model as a similarity reference to determine if the received model is malicious or benign. We establish the theoretical convergence guarantee for BALANCE under poisoning attacks in both strongly convex and non-convex settings. Furthermore, the convergence rate of BALANCE under poisoning attacks matches those of the state-of-the-art counterparts in Byzantine-free settings. Extensive experiments also demonstrate that BALANCE outperforms existing DFL methods and effectively defends against poisoning attacks.