Incentive Analysis for Agent Participation in Federated Learning

2025-03-12

AI Summary

This work addresses the strategic decision-making of clients in federated learning regarding participation in global model training versus performing local training. Method: We formalize this behavior as both a stage game and an infinitely repeated game, introducing for the first time the selection of client participation as a game-theoretic equilibrium problem. We identify data quality similarity as a key driver of collective participation and propose a myopic repeated-game strategy that ensures privacy preservation and low computational overhead. Contribution/Results: Under mild conditions, we prove that the Nash equilibrium achieves optimal social welfare; moreover, our strategy converges within a finite number of rounds to a neighborhood of the stage-game Nash equilibrium. The framework combines theoretical rigor with practical deployability, establishing a novel paradigm for designing incentive-compatible federated learning systems.

Technology Category

• Game Theory and Economic Paradigms: Cooperative Game Theory
• Multiagent Systems: Mechanism Design
• Machine Learning: Distributed Machine Learning & Federated 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
• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing

Abstract

Federated learning offers a decentralized approach to machine learning, where multiple agents collaboratively train a model while preserving data privacy. In this paper, we investigate the decision-making and equilibrium behavior in federated learning systems, where agents choose between participating in global training or conducting independent local training. The problem is first modeled as a stage game and then extended to a repeated game to analyze the long-term dynamics of agent participation. For the stage game, we characterize the participation patterns and identify Nash equilibrium, revealing how data heterogeneity influences the equilibrium behavior-specifically, agents with similar data qualities will participate in FL as a group. We also derive the optimal social welfare and show that it coincides with Nash equilibrium under mild assumptions. In the repeated game, we propose a privacy-preserving, computationally efficient myopic strategy. This strategy enables agents to make practical decisions under bounded rationality and converges to a neighborhood of Nash equilibrium of the stage game in finite time. By combining theoretical insights with practical strategy design, this work provides a realistic and effective framework for guiding and analyzing agent behaviors in federated learning systems.

Problem

• Analyzes agent participation decisions in federated learning systems.
• Explores Nash equilibrium and data heterogeneity impact on participation.
• Proposes a privacy-preserving strategy for practical agent decision-making.

Innovation

• Modeled federated learning as stage and repeated games
• Proposed privacy-preserving, efficient myopic strategy
• Analyzed Nash equilibrium and social welfare dynamics