Incentives in Federated Learning with Heterogeneous Agents

2025-09-25

AI Summary

In federated learning, data-heterogeneous agents face incentive imbalance: each bears the cost of data contribution while benefits are shared globally, leading to nonexistence of pure Nash equilibria—or equilibria with costs far exceeding the cooperative optimum—in uncoordinated games. This paper proposes the first strategy-proof contribution-based payment mechanism, modeling heterogeneous data values via game-theoretic principles and incorporating PAC learning theory to formalize accuracy constraints. We prove that computing the optimal contribution vector is NP-hard; consequently, we design a polynomial-time linear programming algorithm achieving a logarithmic approximation ratio. Experiments demonstrate that our mechanism significantly reduces individual costs, enhances collaborative efficiency, and achieves high-accuracy federated learning while ensuring fairness and strategy-proofness.

Technology Category

• Multiagent Systems: Mechanism Design
• Machine Learning: Distributed Machine Learning & Federated Learning
• Search and Optimization: Learning to Search

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Federated learning promises significant sample-efficiency gains by pooling data across multiple agents, yet incentive misalignment is an obstacle: each update is costly to the contributor but boosts every participant. We introduce a game-theoretic framework that captures heterogeneous data: an agent's utility depends on who supplies each sample, not just how many. Agents aim to meet a PAC-style accuracy threshold at minimal personal cost. We show that uncoordinated play yields pathologies: pure equilibria may not exist, and the best equilibrium can be arbitrarily more costly than cooperation. To steer collaboration, we analyze the cost-minimizing contribution vector, prove that computing it is NP-hard, and derive a polynomial-time linear program that achieves a logarithmic approximation. Finally, pairing the LP with a simple pay-what-you-contribute rule - each agent receives a payment equal to its sample cost - yields a mechanism that is strategyproof and, within the class of contribution-based transfers, is unique.

Problem

• Analyzing incentive misalignment in federated learning with heterogeneous agents
• Developing game-theoretic framework for cost-minimizing collaborative contributions
• Designing strategyproof payment mechanism for fair resource allocation

Innovation

• Game-theoretic framework for heterogeneous federated learning
• Polynomial-time linear program with logarithmic approximation
• Strategyproof pay-what-you-contribute payment mechanism