Incentivized Collaborative Learning: Architectural Design and Insights

Xinran Wang, Qi Le, Ahmad Faraz Khan, Jie Ding, Ali Anwar

Primary Area: societal considerations including fairness, safety, privacy

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Keywords: collaborative learning, incentive, modeling

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TL;DR: We propose a framework for incentivized collaborative learning and develop theory, applications, and insights to demonstrate when and why incentives can improve collaboration performance.

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, an architectural framework for incentivized collaborative learning, and provide insights into the critical issue of when and why incentives can improve collaboration performance. Then, we apply the concepts of ICL to specific use cases in federated learning, assisted learning, and multi-armed bandit, corroborated with both theoretical and experimental results.

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Submission Number: 4013