A Framework for Incentivized Collaborative Learning
This addresses the problem of enabling effective collaboration in machine learning for entities like companies and AI agents, with incremental contributions to existing methods.
The paper tackles the challenge of incentivizing multiple entities to collaborate in collaborative learning before collaboration occurs, proposing the ICL framework and showing its applicability to federated learning, assisted learning, and multi-armed bandit with theoretical and experimental results.
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.