去中心化联邦学习综述

doi:10.3778/j.issn.1673-9418.2501059

摘要/Abstract

摘要： 随着大规模异构数据的快速增长，集中式联邦学习面临数据处理和隐私保护的挑战。去中心化联邦学习通过消除对中心服务器的依赖，增强了系统容错性和适应性，同时分散通信负载，显著提升了隐私保护水平。系统性地阐述了集中式联邦学习与去中心化联邦学习的基本原理，并通过多维度对比分析体现两者差异。在此基础上，深入探讨了去中心化联邦学习在通信优化、隐私保护机制、模型聚合策略等方面的技术优势与创新方法。全面分析了去中心化联邦学习在医疗健康、智能制造、智慧城市等领域的应用前景与发展趋势。通过对比常见去中心化联邦学习框架在常用数据集上的性能，展示了不同框架的优势，并总结了当前主流的开源框架，对未来研究可能面临的技术挑战和发展机遇进行了展望。

关键词: 去中心化联邦学习, 通信机制, 隐私保护, 模型聚合策略, 应用场景

Abstract: With the rapid growth of large-scale heterogeneous data, centralized federated learning faces challenges in data processing and privacy protection. Decentralized federated learning addresses these issues by eliminating reliance on central servers, enhancing system fault tolerance and adaptability, while distributing communication loads and significantly improving privacy protection. This paper systematically elaborates on the fundamental principles of centralized and decentralized federated learning, highlighting their differences through multi-dimensional comparative analysis. Building on this, it delves into the technical advantages and innovative methods of decentralized federated learning in communication optimization, privacy protection mechanisms, and model aggregation strategies. Additionally, it comprehensively analyzes the application prospects and development trends of decentralized federated learning in healthcare, smart manufacturing, and smart cities. Finally, through comparing the performance of prevalent decentralized federated learning frameworks on commonly used datasets, this paper highlights their respective advantages, provides a summary of current mainstream open-source frameworks, and offers perspectives on potential technical challenges and development opportunities for future research.

Key words: decentralized federated learning, communication mechanisms, privacy protection, model aggregation strategy, application scenarios

陈丽芳, 许恺龙, 赵人喆, 韩阳, 代琪. 去中心化联邦学习综述[J]. 计算机科学与探索, 2025, 19(10): 2648-2666.

CHEN Lifang, XU Kailong, ZHAO Renzhe, HAN Yang, DAI Qi. Review of Decentralized Federated Learning[J]. Journal of Frontiers of Computer Science and Technology, 2025, 19(10): 2648-2666.

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