Paid with Models: Optimal Contract Design for Collaborative Machine Learning

作     者：Wang, Bingchen Wu, Zhaoxuan Liu, Fusheng Low, Bryan Kian Hsiang 

作者机构：Institute of Data Science National University of Singapore Singapore Singapore-MIT Alliance for Research and Technology Singapore Department of Computer Science National University of Singapore Singapore 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2024年

核心收录：

主　　题：Collaborative learning 

摘      要：Collaborative machine learning (CML) provides a promising paradigm for democratizing advanced technologies by enabling cost-sharing among participants. However, the potential for rent-seeking behaviors among parties can undermine such collaborations. Contract theory presents a viable solution by rewarding participants with models of varying accuracy based on their contributions. However, unlike monetary compensation, using models as rewards introduces unique challenges, particularly due to the stochastic nature of these rewards when contribution costs are privately held information. This paper formalizes the optimal contracting problem within CML and proposes a transformation that simplifies the non-convex optimization problem into one that can be solved through convex optimization algorithms. We conduct a detailed analysis of the properties that an optimal contract must satisfy when models serve as the rewards, and we explore the potential benefits and welfare implications of these contract-driven CML schemes through numerical experiments. © 2024, CC BY.