提出可应对任意客户端参与的联邦学习方法，无需假设数据分布。
FedSUM Family: Efficient Federated Learning Methods under Arbitrary Client Participation
- 用最大延迟和平均延迟建模客户端参与波动性。
- 三种变体在不同场景下均保持收敛性。
- 适合实际中参与不稳定的分布式学习任务。
联邦学习方法通常针对特定客户端参与模式设计，限制了其在实际部署中的适用性。我们提出 FedSUM 家族算法，可在无需对数据异质性做额外假设的情况下支持任意客户端参与。该框架通过两个延迟指标——最大延迟 $τ_{\max}$ 和平均延迟 $τ_{\text{avg}}$——来建模参与的不确定性。FedSUM 家族包含三个变体：FedSUM-B（基础版）、FedSUM（标准版）和 FedSUM-CR（通信优化版）。我们提供了统一的收敛性保证，证明该方法在多样化参与模式下的有效性，从而拓展了联邦学习在真实场景中的应用范围。
原文摘要 · Abstract (English)
Federated Learning (FL) methods are often designed for specific client participation patterns, limiting their applicability in practical deployments. We introduce the FedSUM family of algorithms, which supports arbitrary client participation without additional assumptions on data heterogeneity. Our framework models participation variability with two delay metrics, the maximum delay $τ_{\max}$ and the average delay $τ_{\text{avg}}$. The FedSUM family comprises three variants: FedSUM-B (basic version), FedSUM (standard version), and FedSUM-CR (communication-reduced version). We provide unified convergence guarantees demonstrating the effectiveness of our approach across diverse participation patterns, thereby broadening the applicability of FL in real-world scenarios.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。