Collaborative learning techniques have significantly advanced in recent years, enabling private model training across multiple organizations. Despite this opportunity, firms face a dilemma when considering data sharing with competitors -- while collaboration can improve a company's machine learning model, it may also benefit competitors and hence reduce profits. In this work, we introduce a general framework for analyzing this data-sharing trade-off. The framework consists of three components, representing the firms' production decisions, the effect of additional data on model quality, and the data-sharing negotiation process, respectively. We then study an instantiation of the framework, based on a conventional market model from economic theory, to identify key factors that affect collaboration incentives. Our findings indicate a profound impact of market conditions on the data-sharing incentives. In particular, we find that reduced competition, in terms of the similarities between the firms' products, and harder learning tasks foster collaboration.

翻译：协作学习技术近年来取得了显著进步，使得多个组织能够进行私有模型训练。尽管存在这一机遇，企业在考虑与竞争对手共享数据时仍面临两难困境——协作虽可提升公司的机器学习模型，但也可能惠及竞争对手，从而降低利润。本文提出一个分析这种数据共享权衡的通用框架。该框架包含三个组成部分，分别代表企业的生产决策、额外数据对模型质量的影响以及数据共享谈判过程。随后，我们基于经济理论中的传统市场模型研究该框架的一个具体实例，以识别影响协作动机的关键因素。研究结果表明，市场条件对数据共享动机具有深远影响。特别是，我们发现：企业产品相似度所反映的竞争程度降低，以及学习任务难度增加，均会促进协作。