Incentivizing Honesty among Competitors in Collaborative Learning and Optimization

TL;DR

This paper models competitive collaborative learning as a game, identifies incentives for dishonesty, and proposes mechanisms to promote honest participation, ensuring effective learning even with strategic, potentially dishonest clients.

Contribution

It introduces a game-theoretic framework for competitive collaborative learning and proposes incentive mechanisms to promote honesty and robust learning.

Findings

Rational clients tend to manipulate updates, hindering learning.

Proposed mechanisms incentivize honest communication among competitors.

Empirical results show the effectiveness of the incentive scheme on federated learning benchmarks.

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.