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.

翻译：各类实体（如公司、研究实验室、AI智能体及边缘设备）之间的协作对于完成单一实体无法独立实现的机器学习任务日益重要。这种需求通常源于安全约束、隐私顾虑及计算资源限制等因素。因此，协作学习研究正蓬勃发展。然而，在协作学习的实际应用中，一个关键挑战在于如何有效激励多个实体在协作发生前参与合作。本研究提出ICL——一个面向激励协作学习的通用框架，并深入剖析了激励措施提升协作性能的时机与原因等重要问题。此外，我们通过理论分析与实验验证，证明了ICL在联邦学习、辅助学习及多臂老虎机等具体场景中的广泛适用性。