Survey of Blockchain-Based Federated Learning System Schemes

doi:10.3778/j.issn.1002-8331.2410-0227

Abstract

Abstract: Federated learning enables multiple participants to collaboratively train models while protecting data privacy, and it has been widely applied in various fields such as healthcare and smart cities, making significant contributions to breaking the“data silo”phenomenon. However, in practical applications, federated learning faces issues such as single points of failure, susceptibility to malicious attacks, and data privacy breaches. To address these, researchers have considered leveraging blockchain technology, which is characterized by decentralization, immutability, and high transparency, to provide a secure data exchange platform for federated learning. However, the integrated architecture still encounters challenges such as the lack of effective incentive mechanisms, high computational and storage costs, and the absence of malicious model detection mechanisms. Through in-depth research and exploration of the potential and challenges of blockchain-based federated learning systems, this paper discusses the optimization and improvement of consensus mechanisms, privacy protection schemes, network security measures, incentive mechanisms, and secure aggregation methods within the architecture, starting from the existing problems in different frameworks and technologies. Subsequently, the paper elaborates on the key role of smart contracts in automated execution, contribution assessment, and reward distribution. Finally, it identifies challenges in client selection, data heterogeneity processing, privacy protection, reward distribution, and blockchain pain points, and provides an outlook on future research directions.

Key words: decentralized federated learning, blockchain, privacy protection, incentive mechanism, smart contract

摘要： 联邦学习允许多个参与方在保护数据隐私的同时共同训练模型，并广泛应用于如医疗健康、智能城市等诸多领域，为打破“数据孤岛”现象做出了重要贡献。然而，在实际应用中联邦学习存在单点故障、易受恶意攻击和数据隐私泄露等问题。为此，以具有去中心化、不可篡改和高透明度特点的区块链技术为联邦学习提供安全的数据交换平台，但集成后的架构仍然存在缺乏有效激励机制、计算存储成本高、缺乏恶意模型检测机制等问题。通过深入调研与探索基于区块链的联邦学习体系的潜力与挑战，从现有的不同框架与技术存在的问题入手，阐述了为提高整个系统的效率、安全性和公平性，对架构中的共识机制、隐私保护方案、网络安全措施、激励机制、安全聚合方法等方面的优化和改进；阐述了智能合约在自动化执行、贡献评估和奖励分配中的关键作用。最后，总结在客户端选择、数据异质化处理、隐私保护、奖励分配和区块链痛点等方面的挑战，并对未来的研究方向进行了展望。

关键词: 去中心化联邦学习, 区块链, 隐私保护, 激励机制, 智能合约

GUO Siyun, LI Leixiao, DU Jinze, LIN Hao. Survey of Blockchain-Based Federated Learning System Schemes[J]. Computer Engineering and Applications, 2025, 61(15): 36-53.

郭思昀, 李雷孝, 杜金泽, 林浩. 基于区块链的联邦学习系统方案研究综述[J]. 计算机工程与应用, 2025, 61(15): 36-53.

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