Review of communication optimization methods in federated learning

YANG Zhikai; LIU Yaping; ZHANG Shuo; SUN Zhe; YAN Dingyu

doi:10.11959/j.issn.2096-109x.2024077

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Review of communication optimization methods in federated learning

Reviews | 更新时间：2025-02-06

- Review of communication optimization methods in federated learning
- Chinese Journal of Network and Information Security Vol. 10, Issue 6, Pages: 1-23(2024)
- 作者机构：
  
  1.广州大学网络空间安全学院，广东广州 510006
  2.可信分布式计算与服务教育部重点实验室（北京邮电大学），北京 100876
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(62372124);Key-Area Research and Development Program of Guangdong Province(2019B010137005)
- DOI：10.11959/j.issn.2096-109x.2024077
  CLC： TP393
- Received：27 May 2024，
  
  Revised：2024-11-05，
  
  Published：25 December 2024
- 稿件说明：
移动端阅览
杨智凯, 刘亚萍, 张硕, 等. 联邦学习通信优化方法综述[J]. 网络与信息安全学报, 2024, 10(6): 1-23.

YANG Z K, LIU Y P, ZHANG S, et al. Review of communication optimization methods in federated learning[J]. Chinese Journal of Network and Information Security, 2024, 10(6): 1-23.
杨智凯, 刘亚萍, 张硕, 等. 联邦学习通信优化方法综述[J]. 网络与信息安全学报, 2024, 10(6): 1-23. DOI： 10.11959/j.issn.2096-109x.2024077.

YANG Z K, LIU Y P, ZHANG S, et al. Review of communication optimization methods in federated learning[J]. Chinese Journal of Network and Information Security, 2024, 10(6): 1-23. DOI： 10.11959/j.issn.2096-109x.2024077.

摘要

随着以深度学习为代表的人工智能技术的发展和普及，其不断暴露的安全问题已经成为影响网络空间安全的巨大挑战。传统的以云为中心的分布式机器学习通过收集参与方的数据来训练模型或优化模型表现，在数据交换过程中容易受到安全攻击和隐私攻击等网络安全风险的影响，进而造成整体系统效能下降或隐私数据泄露等后果。联邦学习作为一种具有隐私保护能力的分布式机器学习模式，通过客户端与参数服务器的频繁通信进行模型参数交换，在原始数据不离开本地的情况下训练联合模型，这极大降低了隐私数据泄露风险，在一定程度上保证了数据安全。但是随着深度学习模型的规模越来越大，联邦学习任务越来越复杂，通信开销也逐渐增加，最终成为联邦学习应用落地的阻碍。因此，针对联邦学习通信优化方法的探索成为联邦学习研究中的热点。介绍了联邦学习的技术背景和工作流程、分析了其通信瓶颈的来源和造成的影响。根据影响通信效率的因素，从模型参数压缩、模型更新策略、系统架构和通信协议等优化目标对现有联邦学习通信优化方法进行全面梳理和分析，并给出了该研究领域的发展脉络。最后总结了现有联邦学习通信优化方法所面临的问题，并对未来的发展趋势与研究方向进行展望。

Abstract

With the development and popularization of artificial intelligence technologies represented by deep learning

the security issues they continuously expose have become a huge challenge affecting cyberspace security. Traditional cloud-centric distributed machine learning

which trains models or optimizes model performance by collecting data from participating parties

is susceptible to security attacks and privacy attacks during the data exchange process

leading to consequences such as a decline in overall system efficiency or the leakage of private data. Federated learning

as a distributed machine learning paradigm with privacy protection capabilities

exchanges model parameters through frequent communication between clients and parameter servers

training a joint model without the raw data leaving the local area. This greatly reduces the risk of private data leakage and ensures data security to a certain extent. However

as deep learning models become larger and federated learning tasks more complex

communication overhead also increases

eventually becoming a barrier to the application of federated learning. Therefore

the exploration of communication optimization methods for federated learning has become a hot topic. The technical background and workflow of federated learning were introduced

and the sources and impacts of its communication bottlenecks were analyzed. Then

based on the factors affecting communication efficiency

existing federated learning communication optimization methods were comprehensively sorted out and analyzed from optimization objectives such as model parameter compression

model update strategies

system architecture

and communication protocols. The development trend of this research field was also presented. Finally

the problems faced by existing federated learning communication optimization methods were summarized

and future development trends and research directions were looked forward to.

关键词

Keywords

references

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