Collaborative learning offers a promising avenue for leveraging decentralized data. However, collaboration in groups of strategic learners is not a given. In this work, we consider strategic agents who wish to train a model together but have sampling distributions of different quality. The collaboration is organized by a benevolent aggregator who gathers samples so as to maximize total welfare, but is unaware of data quality. This setting allows us to shed light on the deleterious effect of adverse selection in collaborative learning. More precisely, we demonstrate that when data quality indices are private, the coalition may undergo a phenomenon known as unravelling, wherein it shrinks up to the point that it becomes empty or solely comprised of the worst agent. We show how this issue can be addressed without making use of external transfers, by proposing a novel method inspired by probabilistic verification. This approach makes the grand coalition a Nash equilibrium with high probability despite information asymmetry, thereby breaking unravelling.

翻译：协作学习为利用分散数据提供了一条前景广阔的途径。然而，在由策略性学习者组成的群体中，协作并非必然。在本研究中，我们考虑了一组希望共同训练模型但具有不同质量采样分布的策略性智能体。协作由一个仁慈的聚合者组织，该聚合者以最大化总体福利为目标收集样本，但无法获知数据质量信息。这一设定使我们得以揭示协作学习中逆向选择的有害影响。更具体而言，我们证明了当数据质量指标为私有信息时，联盟可能经历一种称为"解体"的现象——联盟规模不断缩小直至变为空集或仅包含最劣质智能体。我们展示了如何在不借助外部转移支付的情况下，通过提出一种受概率验证启发的新方法来解决该问题。该方法使得大联盟在信息不对称情况下以高概率成为纳什均衡，从而打破解体困境。