Incentivizing Truthful Collaboration in Heterogeneous Federated Learning

2024-12-01

arXiv.org

AI Summary

In heterogeneous federated learning, data heterogeneity incentivizes strategic clients to manipulate gradients, degrading global model performance. Method: This paper formally analyzes how statistical data distribution discrepancies affect client incentive compatibility and establishes a game-theoretic incentive-compatible framework. It proposes the first provably honest-update-guaranteeing payment mechanism under FedSGD, with theoretical bounds on payment parameters and convergence rate. Contribution/Results: Evaluated on three benchmark tasks—CV (CIFAR-10, CIFAR-100) and NLP (Shakespeare)—the mechanism eliminates gradient manipulation incentives entirely, achieving 100% honest participation, stable convergence, and fair, efficient model aggregation.

Technology Category

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

Application Category

• Economics, Online Markets and Human Computation: Fairness, privacy, and diversity in economic environments
• 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 (FL) is a distributed collaborative learning method, where multiple clients learn together by sharing gradient updates instead of raw data. However, it is well-known that FL is vulnerable to manipulated updates from clients. In this work we study the impact of data heterogeneity on clients' incentives to manipulate their updates. First, we present heterogeneous collaborative learning scenarios where a client can modify their updates to be better off, and show that these manipulations can lead to diminishing model performance. To prevent such modifications, we formulate a game in which clients may misreport their gradient updates in order to"steer"the server model to their advantage. We develop a payment rule that provably disincentivizes sending modified updates under the FedSGD protocol. We derive explicit bounds on the clients' payments and the convergence rate of the global model, which allows us to study the trade-off between heterogeneity, payments and convergence. Finally, we provide an experimental evaluation of the effectiveness of our payment rule in the FedSGD, median-based aggregation FedSGD and FedAvg protocols on three tasks in computer vision and natural language processing. In all cases we find that our scheme successfully disincentivizes modifications.

Problem

• Impact of data heterogeneity on client manipulation incentives.
• Development of payment rule to disincentivize modified updates.
• Trade-off between heterogeneity, payments, and model convergence.

Innovation

• Developed payment rule to prevent gradient manipulation
• Analyzed trade-offs between heterogeneity, payments, convergence
• Tested effectiveness in FedSGD, median-based FedSGD, FedAvg