Research Status of Consensus Mechanisms in Federated Learning

doi:10.3778/j.issn.1673-9418.2504063

Abstract

Abstract: 1. School of Information and Electronic Technology, Jiamusi University, Jiamusi, Heilongjiang 154007, China
2. Heilongjiang Province Key Laboratory of Autonomous Intelligence and Information Processing, School of Information and Electronic Technology, Jiamusi University, Jiamusi, Heilongjiang 154007, China
3. Jiamusi Key Laboratory of Satellite Navigation Technology and Equipment Engineering Technology, Jiamusi, Heilongjiang 154007, China

Key words: consensus mechanism, blockchain, federated learning

摘要： 联邦学习的快速发展带来了分布式数据协同训练的新机遇。区块链为联邦学习的中心化信任、数据隐私保护、系统安全及通信开销优化等关键挑战带来了解决思路，成为联邦学习的一个重要组成部分。而共识机制作为区块链在联邦学习中的重要组成，可以对模型更新进行验证，实现去中心化聚合，使用激励确保参与者积极参与等。针对联邦学习中共识机制的差异，本文从联邦学习中的经典共识、表现共识、协议共识与协同共识四个角度对现有的联邦学习中使用的共识方案进行了比较，经典共识侧重于区块生成与基础一致性保障；表现共识根据节点本地训练表现进行参与度调节；协议共识面向联邦任务设计专属策略，实现定制化高效聚合；协同共识则支持多链多域环境下的跨链验证与一致性维护。通过对比联邦学习中不同共识的代表算法、优缺点以及适用环境，分析出当前共识机制存在隐私嵌入困难、通信负担重、异构适应性差等问题。最后提出机制融合、轻量设计、隐私集成等未来优化方向，为共识机制与联邦学习的深度融合提供理论支撑与方法参考。

关键词: 共识机制, 区块链, 联邦学习

LIU YI, WU Shiwei, JIANG Chengjie, DONG Huiting, WU Yinmiao, GUAN Xinru, JIANG Sheng, ZHANG Lei. Research Status of Consensus Mechanisms in Federated Learning[J]. Journal of Frontiers of Computer Science and Technology, 2025, 19(11): 2913-2934.

刘义, 吴世伟, 蒋澄杰, 董慧婷, 吴银淼, 管新如, 蒋胜, 张磊. 共识机制在联邦学习中的研究现状[J]. 计算机科学与探索, 2025, 19(11): 2913-2934.

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