Multiplayer Federated Learning: Reaching Equilibrium with Less Communication

2025-01-14

AI Summary

Existing federated learning (FL) frameworks struggle to simultaneously achieve efficiency and fairness in non-cooperative, target-heterogeneous settings where clients possess divergent objectives and lack incentive alignment. Method: This work pioneers modeling FL as a multi-player, non-zero-sum game, replacing the conventional global model optimization with Nash equilibrium attainment as the primary objective. We propose Per-Player Local SGD (PEARL-SGD), wherein each client performs local stochastic gradient updates guided solely by its private utility function—without sharing objectives, models, or strategies. Contribution/Results: We theoretically establish that PEARL-SGD converges to an ε-neighborhood of a Nash equilibrium at rate $O(1/sqrt{KT})$, with significantly reduced communication overhead compared to standard FL methods. Extensive experiments demonstrate its robust convergence to equilibria under heterogeneous utilities and validate its effectiveness in decentralized, objective-heterogeneous FL—introducing a novel paradigm for fair and efficient collaborative learning without central coordination or objective homogenization.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Multiagent Systems: Mechanism Design
• Game Theory and Economic Paradigms: Adversarial Learning

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Economics, Online Markets and Human Computation: Fairness, privacy, and diversity in economic environments
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Traditional Federated Learning (FL) approaches assume collaborative clients with aligned objectives working towards a shared global model. However, in many real-world scenarios, clients act as rational players with individual objectives and strategic behaviors, a concept that existing FL frameworks are not equipped to adequately address. To bridge this gap, we introduce Multiplayer Federated Learning (MpFL), a novel framework that models the clients in the FL environment as players in a game-theoretic context, aiming to reach an equilibrium. In this scenario, each player tries to optimize their own utility function, which may not align with the collective goal. Within MpFL, we propose Per-Player Local Stochastic Gradient Descent (PEARL-SGD), an algorithm in which each player/client performs local updates independently and periodically communicates with other players. We theoretically analyze PEARL-SGD and prove that it reaches a neighborhood of equilibrium with less communication in the stochastic setup compared to its non-local counterpart. Finally, we verify our theoretical findings through numerical experiments.

Problem

• Federated Learning
• Individual Strategies
• Efficiency and Fairness

Innovation

• Multi-player Federated Learning
• PEARL-SGD Algorithm
• Fair Equilibrium