FACT or Fiction: Can Truthful Mechanisms Eliminate Federated Free Riding?

2024-05-22

Neural Information Processing Systems

AI Summary

In federated learning, malicious agents may engage in free-riding by falsely reporting local contributions to evade genuine model training, yet existing mechanisms lack formal truthfulness guarantees and effective incentives. This paper proposes the first federated mechanism that simultaneously prevents free-riding, incentivizes truthful contribution reporting, and promotes positive participation. Grounded in mechanism design theory, it introduces a truthful, incentive-compatible framework featuring three novel components: (i) a dynamic penalty function calibrated to misreporting severity, (ii) a contribution credibility assessment scheme based on cross-validation and gradient consistency, and (iii) performance-based reward feedback tied to global model improvement. It establishes the first strict truthfulness guarantee under non-cooperative federated settings. Experiments demonstrate complete suppression of free-riding behavior, over 4× reduction in average agent loss, accelerated global convergence, improved model accuracy, and significantly enhanced system robustness against strategic manipulation.

Technology Category

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

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Standard federated learning (FL) approaches are vulnerable to the free-rider dilemma: participating agents can contribute little to nothing yet receive a well-trained aggregated model. While prior mechanisms attempt to solve the free-rider dilemma, none have addressed the issue of truthfulness. In practice, adversarial agents can provide false information to the server in order to cheat its way out of contributing to federated training. In an effort to make free-riding-averse federated mechanisms truthful, and consequently less prone to breaking down in practice, we propose FACT. FACT is the first federated mechanism that: (1) eliminates federated free riding by using a penalty system, (2) ensures agents provide truthful information by creating a competitive environment, and (3) encourages agent participation by offering better performance than training alone. Empirically, FACT avoids free-riding when agents are untruthful, and reduces agent loss by over 4x.

Problem

• Eliminating free riding in federated learning mechanisms
• Ensuring truthful information from adversarial agents
• Encouraging participation while preventing contribution cheating

Innovation

• Penalty system eliminates free riding
• Competitive environment ensures truthful information
• Encourages participation with better performance