Pathwise Individual Rationality in Federated Learning: A Mechanism-Architecture Co-Design

2026-09-13

AI Summary

本文探讨了联邦学习中个体理性问题，通过结合短期参与保证与个性化模型评估的方法，解决了客户在参与过程中可能遭遇的效用损失问题。

Abstract

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, not just as an asymptotic equilibrium guarantee under different models of client data distribution heterogeneity. Along this 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, and we prove that it backfires, collapsing learning even at low-to-modest heterogeneity. We then propose a novel design that combines short-term participation guarantees with personalized model evaluation, while maintaining fair incentives. We provide a theoretical basis for this new approach and empirically demonstrate that clients can avoid short-term losses without harming overall performance, even under moderate data distribution heterogeneity; under severe heterogeneity, the design shows promising outcomes for clients compared to their local baseline at some cost in accuracy.

Problem

• federated learning
• individual rationality
• data distribution heterogeneity
• cost-benefit tradeoff

Innovation

• individual rationality
• federated learning
• personalized model evaluation
• data distribution heterogeneity