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.

本文提出 ICL—— 一种激励协同学习的通用框架，研究为什么和何时激励能够提高合作效果，并通过理论和实验结果展示了 ICL 在联邦学习、协同辅助学习和多臂赌博机等情景下的广泛应用。