A Framework for Incentivized Collaborative Learning
Xinran Wang Qi Le Ahmad Faraz Khan Jie Ding Ali Anwar
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.