发表机构
The Ohio State University（俄亥俄州立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对联邦学习中客户端因短期损失而退出问题，提出机制与架构协同设计，结合短期参与保证与个性化评估，在中等异质性下避免损失且不损性能，严重异质性下以精度代价换取客户端收益。
AI 中文摘要
参与联邦学习（FL）需要付出代价。客户端以隐私、通信和计算成本为代价，换取模型效能可能更大的提升。本文在个体理性（IR）与自给自足（autarky）的框架下探讨这一权衡，即联邦必须提供不劣于本地训练的效用，这是基本的博弈论要求。以该要求为设计目标，我们考察了FL的路径级性能，将其作为累积盈余的每轮界限，而不仅仅是在客户端数据分布异质性的不同模型下的渐近均衡保证。沿着这条路径，客户端可能在数百轮中保持低于其本地训练基线。自然的补救措施是限制每个客户端每轮的贡献，以使这种不足保持有界，但我们证明这适得其反，即使在低至中等异质性下也会导致学习崩溃。随后，我们提出了一种新颖的设计，将短期参与保证与个性化模型评估相结合，同时维持公平激励。我们为这种新方法提供了理论基础，并实证表明，即使在中等数据分布异质性下，客户端也能避免短期损失而不损害整体性能；在严重异质性下，该设计以一定的精度代价为客户端带来了相对于其本地基线的有前景的结果。
英文摘要
Participation in federated learning (FL) comes at a cost. Clients trade off privacy, communication, and compute costs for potentially greater gains in model efficacy. This paper explores this tradeoff under the aegis of individual rationality (IR) versus autarky, the basic game-theoretic requirement that the federation provide utility no worse than local training. Using the above as the design target, we examine pathwise performance of FL, as a per-round bound on cumulative surplus rather than only an asymptotic equilibrium guarantee under different degrees of client data heterogeneity. Along the learning path, clients can remain below their local-training baseline for hundreds of rounds. The natural remedy is to cap each client's per-round contribution so that this shortfall stays bounded. We prove that such a cap drives all contributions to zero, with high probability, when its tolerance is small relative to the warm-up deficit, and we observe learning collapse under it even at low heterogeneity. We then propose a design that combines a short-term participation guarantee, enforced after an announced grace window, with personalized model evaluation, while preserving the incentive properties of the underlying mechanism. We provide a theoretical basis for this approach and empirically demonstrate that, after the grace window, clients meet IR on nearly all rounds without harming overall performance under low to moderate heterogeneity; under severe heterogeneity, the design shows promising outcomes for clients compared to their local baseline at some cost in accuracy.
Comments27 pages, 9 figures, 8 tables
