ICL: An Incentivized Collaborative Learning Framework

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 has been gaining momentum. However, a significant challenge in practical applications of Collaborative Learning 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. We showcase the concepts of ICL to specific use cases in federated learning, assisted learning, and multi-armed bandit, corroborating with both theoretical and experimental results.