A Framework for Incentivized Collaborative Learning

TL;DR

This paper introduces ICL, a comprehensive framework designed to incentivize collaboration among diverse entities in machine learning, addressing practical challenges and demonstrating broad applicability through theoretical and experimental validation.

Contribution

The paper presents a novel incentivized collaborative learning framework, ICL, that enhances cooperation among entities in federated learning, assisted learning, and multi-armed bandits.

Findings

ICL improves collaboration performance when incentives are properly aligned.

The framework is applicable to federated learning, assisted learning, and multi-armed bandits.

Experimental results validate the theoretical insights of ICL.

Abstract

Collaborations among various entities, such as companies, research labs, AI agents, and edge devices, have become increasingly crucial for achieving machine learning tasks that cannot be accomplished by a single entity alone. This is likely due to factors such as security constraints, privacy concerns, and limitations in computation resources. As a result, collaborative learning (CL) research has been gaining momentum. However, a significant challenge in practical applications of CL is how to effectively incentivize multiple entities to collaborate before any collaboration occurs. In this study, we propose ICL, a general framework for incentivized collaborative learning, and provide insights into the critical issue of when and why incentives can improve collaboration performance. Furthermore, we show the broad applicability of ICL to specific cases in federated learning, assisted learning, and multi-armed bandit with both theory and experimental results.