Federated learning design improves client gains without hurting performance
Pathwise Individual Rationality in Federated Learning: A Mechanism-Architecture Co-Design
Machine LearningComputer Science and Game Theory
Summary
Federated learning lets many devices or users train a shared model without sharing their private data, but joining this process can cost clients in privacy and computing. The authors study how to make sure clients don't lose out compared to training alone, especially early on when learning is unstable. They find that simply limiting clients’ contributions to avoid losses can stop learning altogether. Instead, they propose a new approach that guarantees clients won't lose immediately and fairly measures their personal progress, helping clients avoid short-term losses while keeping good overall results.
What this means in practice
- •For mobile app developers: Design federated learning schemes that keep users engaged by ensuring they see benefits over local-only models early and throughout training.
- •For distributed machine learning engineers: Implement client participation mechanisms that balance fairness and learning progress in environments with varying client data quality and distribution.
Authors
Amin Meghrazi, Srinivasan Parthasarathy, Andrew Perrault
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.