Byzantine-Robust Decentralized Federated Learning
<|reference_start|>Byzantine-Robust Decentralized Federated Learning: 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.<|reference_end|>
@article{fang2024byzantine-robust,
    title={Byzantine-Robust Decentralized Federated Learning},
    author={Minghong Fang, Zifan Zhang, Hairi, Prashant Khanduri, Jia Liu, Songtao
  Lu, Yuchen Liu, and Neil Gong},
    journal={arXiv preprint arXiv:2406.10416},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.10416},
    primaryClass={cs.CR cs.DC cs.LG}
}