mlsanityData to 14 Oct 2026

Model-Consistent Byzantine-Resilient Decentralized Federated Learning for Collaborative Missions

arXiv 2608.22552 ↗First posted 23 Aug 2026cs.DC cs.CR cs.LGcited by 1

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Abstract (arXiv)

Decentralized federated learning (DFL) is a promising paradigm for autonomous nodes to collaboratively train AI models without relying on a central server. However, existing DFL solutions do not guarantee global model consistency, a critical requirement for collaborative mission-critical scenarios where model divergence undermines decision uniformity and safety. This lack of consistency also amplifies vulnerability to Byzantine adversaries, who exploit the decentralized network topology and weak synchrony to perform equivocation and model poisoning attacks against individual victims. This paper introduces DFL-C, a novel Byzantine-resilient DFL architecture that enables decentralized nodes to perform collaborative training with global model consistency. At its core, DFL-C integrates an asynchronous common subset (ACS) consensus protocol into the DFL workflow to ensure all nodes aggregate a uniform set of model updates to establish global model consistency, despite individual Byzantine equivocation. DFL-C further implements a dual-domain trust scoring mechanism to provide resilience against data-domain Byzantine manipulations including model poisoning attacks. This mechanism complements the consensus protocol, significantly reducing the latter's runtime. Our experimental results demonstrate that DFL-C maintains model accuracy while achieving global model consistency under Byzantine behaviors with moderate consensus overhead. Notably, when compared with the state-of-the-art DFL solution BALANCE (Fang et al.) that does not provide model consistency, DFL-C achieves better model accuracy against untargeted model poisoning attacks and comparable resilience against backdoor attacks, with the advantage widened under non-IID scenarios.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 26 of its 40 references that are in the map. First posted on arXiv 23 Aug 2026.

Builds on

26 of 40 references.

References · 26

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  3. 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,349
  4. 2018Byzantine-Robust Distributed Learning: Towards Optimal Statistical Ratescited by 711
  5. 2019Local Model Poisoning Attacks to Byzantine-Robust Federated Learningcited by 493
  6. 2018Analyzing Federated Learning through an Adversarial Lenscited by 351
  7. 2020FLTrust: Byzantine-robust Federated Learning via Trust Bootstrappingcited by 346
  8. 2020Data Poisoning Attacks Against Federated Learning Systemscited by 275
  9. 2019Asynchronous Federated Optimizationcited by 267
  10. 2022Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and Challengescited by 138
  11. 2022FLDetector: Defending Federated Learning Against Model Poisoning Attacks via Detecting Malicious Clientscited by 125
  12. 2021Robbing the Fed: Directly Obtaining Private Data in Federated Learning with Modified Modelscited by 108
14 more
  1. 2020A Blockchain-based Decentralized Federated Learning Framework with Committee Consensuscited by 84
  2. 2016Decentralized Collaborative Learning of Personalized Models over Networkscited by 76
  3. 2020Collaborative Learning in the Jungle (Decentralized, Byzantine, Heterogeneous, Asynchronous and Nonconvex Learning)cited by 54
  4. 2021Deep Federated Learning for Autonomous Drivingcited by 54
  5. 2021Semi-Decentralized Federated Learning with Cooperative D2D Local Model Aggregationscited by 51
  6. 2024Byzantine-Robust Decentralized Federated Learningcited by 48
  7. 2023Improving the Model Consistency of Decentralized Federated Learningcited by 45
  8. 2022Byzantine-Robust Decentralized Learning via ClippedGossipcited by 31
  9. 2020Byzantine-resilient Decentralized Stochastic Gradient Descentcited by 28
  10. 2023FreqFed: A Frequency Analysis-Based Approach for Mitigating Poisoning Attacks in Federated Learningcited by 27
  11. 2022SWIFT: Rapid Decentralized Federated Learning via Wait-Free Model Communicationcited by 21
  12. 2022Semi-Decentralized Federated Learning with Collaborative Relayingcited by 16
  13. 2023AEDFL: Efficient Asynchronous Decentralized Federated Learning with Heterogeneous Devicescited by 15
  14. 2024DRACO: Decentralized Asynchronous Federated Learning over Row-Stochastic Wireless Networkscited by 5

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Citing papers per year: 2026: 1.

1 paper.

  1. 2026Byzantine-Resilient Federated Large Language Model via Hypergraph Message Passingcited by 0

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