系统梳理联邦学习中部分客户端参与的挑战与应对方法
Overcoming Challenges of Partial Client Participation in Federated Learning : A Comprehensive Review
- 按参与程度分类现有方法，分析其适用场景
- 指出真实场景下部分参与会降低模型收敛速度和精度
- 适合关注实际部署问题的研究者与工程人员
联邦学习（FL）是一种分布式训练机制，可在不共享原始数据的前提下协同训练全局模型。本文系统综述了部分客户端参与对联邦学习的影响。尽管现有研究多聚焦于全量客户端参与下的数据异构性带来的泛化、鲁棒性和公平性问题，但对现实中常见的部分客户端参与所引发的理论与实践挑战关注较少。本综述深入分析了针对部分参与设计的现有联邦学习方法，结合理论推导与实证结果，提供结构化分类，揭示各类方法的优势与局限。
原文摘要 · Abstract (English)
Federated Learning (FL) is a learning mechanism that falls under the distributed training umbrella, which collaboratively trains a shared global model without disclosing the raw data from different clients. This paper presents an extensive survey on the impact of partial client participation in federated learning. While much of the existing research focuses on addressing issues such as generalization, robustness, and fairness caused by data heterogeneity under the assumption of full client participation, limited attention has been given to the practical and theoretical challenges arising from partial client participation, which is common in real-world scenarios. This survey provides an in-depth review of existing FL methods designed to cope with partial client participation. We offer a comprehensive analysis supported by theoretical insights and empirical findings, along with a structured categorization of these methods, highlighting their respective advantages and disadvantages.
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