用模型奖励设计最优合约，解决协作机器学习中的利益争抢问题。
Paid with Models: Optimal Contract Design for Collaborative Machine Learning
- 以模型精度作为激励，根据贡献度动态分配不同性能的模型。
- 将非凸优化问题转化为可解的凸优化，实现高效合约求解。
- 适用于需公平激励的分布式模型协作场景，如多方联合训练。
协作机器学习（CML）通过参与者间成本分担，为普及先进技术提供了可行路径。然而，各方可能产生租金攫取行为，破坏合作。合同理论提出一种解决方案：根据贡献度，以不同准确率的模型作为奖励。与货币补偿不同，模型奖励具有随机性，且在贡献成本为私有信息时带来独特挑战。本文形式化了CML中的最优合约设计问题，提出一种变换方法，将非凸优化问题转化为可通过凸优化算法求解的形式。我们详细分析了以模型为奖励时最优合约必须满足的性质，并通过数值实验探索了此类合约驱动的CML方案的潜在收益与福利影响。
原文摘要 · Abstract (English)
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.
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