Gradient Manipulation in Distributed Stochastic Gradient Descent with Strategic Agents: Truthful Incentives with Convergence Guarantees

2026-03-29

AI Summary

This work addresses the challenge of strategic manipulation in decentralized stochastic gradient descent (SGD), where selfish agents may falsify gradients to gain personal advantage, thereby compromising model convergence and performance. The paper proposes a fully decentralized incentive mechanism that encourages agents to truthfully report their local gradients without relying on a central server, while simultaneously ensuring both algorithmic convergence and learning accuracy. It establishes the first theoretical guarantees in distributed SGD for both truthfulness and convergence precision, overcoming key limitations of prior approaches that either depend on centralized architectures or sacrifice learning accuracy. Theoretical analysis demonstrates that the cumulative gain from strategic behavior is bounded and that the convergence rate remains controllable. Empirical evaluations on standard machine learning tasks and benchmark datasets validate the effectiveness of the proposed method.

Technology Category

Multiagent Systems: Mechanism DesignMachine Learning: Distributed Machine Learning & Federated LearningGame Theory and Economic Paradigms: Mechanism Design

Application Category

Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystemsSearch and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for rankingSecurity and Privacy: Large-scale security measurements

Abstract

Distributed learning has gained significant attention due to its advantages in scalability, privacy, and fault tolerance.In this paradigm, multiple agents collaboratively train a global model by exchanging parameters only with their neighbors. However, a key vulnerability of existing distributed learning approaches is their implicit assumption that all agents behave honestly during gradient updates. In real-world scenarios, this assumption often breaks down, as selfish or strategic agents may be incentivized to manipulate gradients for personal gain, ultimately compromising the final learning outcome. In this work, we propose a fully distributed payment mechanism that, for the first time, guarantees both truthful behaviors and accurate convergence in distributed stochastic gradient descent. This represents a significant advancement, as it overcomes two major limitations of existing truthfulness mechanisms for collaborative learning:(1) reliance on a centralized server for payment collection, and (2) sacrificing convergence accuracy to guarantee truthfulness. In addition to characterizing the convergence rate under general convex and strongly convex conditions, we also prove that our approach guarantees the cumulative gain that an agent can obtain through strategic behavior remains finite, even as the number of iterations approaches infinity--a property unattainable by most existing truthfulness mechanisms. Our experimental results on standard machine learning tasks, evaluated on benchmark datasets, confirm the effectiveness of the proposed approach.

Problem

• Distributed Learning
• Strategic Agents
• Gradient Manipulation
• Truthfulness
• Convergence

Innovation

• distributed stochastic gradient descent
• truthful incentives
• convergence guarantees
• strategic agents
• decentralized payment mechanism