Incentivizing Honesty among Competitors in Collaborative Learning and Optimization

2023-05-25

Neural Information Processing Systems

AI Summary

This paper addresses the problem of strategic participants in collaborative learning who submit misleading model updates to gain competitive advantage, thereby degrading others’ performance. Unlike prior work assuming malicious adversaries, we model participants as self-interested yet non-malicious rational agents and propose the first incentive-compatible mechanism for federated learning. Leveraging game-theoretic analysis and mechanism design, we rigorously prove that truthful updates constitute a Nash equilibrium—both in single-round mean estimation and multi-round strongly convex SGD. Theoretically, the mechanism guarantees learning performance approaching that of full cooperation. Empirically, we validate its effectiveness on non-convex federated benchmarks (e.g., FEMNIST): it substantially mitigates strategic manipulation, achieves convergence rates comparable to fully cooperative training, and seamlessly integrates with standard FedAvg without architectural modification.

Abstract

Collaborative learning techniques have the potential to enable training machine learning models that are superior to models trained on a single entity's data. However, in many cases, potential participants in such collaborative schemes are competitors on a downstream task, such as firms that each aim to attract customers by providing the best recommendations. This can incentivize dishonest updates that damage other participants' models, potentially undermining the benefits of collaboration. In this work, we formulate a game that models such interactions and study two learning tasks within this framework: single-round mean estimation and multi-round SGD on strongly-convex objectives. For a natural class of player actions, we show that rational clients are incentivized to strongly manipulate their updates, preventing learning. We then propose mechanisms that incentivize honest communication and ensure learning quality comparable to full cooperation. Lastly, we empirically demonstrate the effectiveness of our incentive scheme on a standard non-convex federated learning benchmark. Our work shows that explicitly modeling the incentives and actions of dishonest clients, rather than assuming them malicious, can enable strong robustness guarantees for collaborative learning.

Problem

• Incentivizing honesty in collaborative learning
• Preventing manipulation in competitive environments
• Ensuring robust collaborative model training

Innovation

• Game theory models interactions
• Mechanisms ensure honest communication
• Empirical validation on federated learning