Collaborative learning techniques have the potential to enable training machine learning models that are superior to models trained on a single entity's data. However, in many cases, potential participants in such collaborative schemes are competitors on a downstream task, such as firms that each aim to attract customers by providing the best recommendations. This can incentivize dishonest updates that damage other participants' models, potentially undermining the benefits of collaboration. In this work, we formulate a game that models such interactions and study two learning tasks within this framework: single-round mean estimation and multi-round SGD on strongly-convex objectives. For a natural class of player actions, we show that rational clients are incentivized to strongly manipulate their updates, preventing learning. We then propose mechanisms that incentivize honest communication and ensure learning quality comparable to full cooperation. Lastly, we empirically demonstrate the effectiveness of our incentive scheme on a standard non-convex federated learning benchmark. Our work shows that explicitly modeling the incentives and actions of dishonest clients, rather than assuming them malicious, can enable strong robustness guarantees for collaborative learning.

翻译：协作学习技术有望使训练出的机器学习模型优于仅基于单个实体数据训练的模型。然而，在许多情况下，此类协作方案的潜在参与者在下游任务中是竞争对手，例如，各企业均旨在通过提供最佳推荐来吸引客户。这会激励参与者进行可能损害其他参与者模型的欺骗性更新，从而削弱协作的益处。在本工作中，我们构建了一个博弈模型来模拟此类交互，并在此框架下研究了两个学习任务：单轮均值估计和多轮强凸目标上的随机梯度下降。针对一类自然的参与者行为，我们证明理性客户有强烈动机操纵其更新，从而阻碍学习。随后，我们提出了激励诚实通信并确保学习质量接近完全协作的机制。最后，我们在标准非凸联邦学习基准上实证展示了所提出激励方案的有效性。我们的研究表明，显式建模不诚实客户的激励和行为（而非假定其存在恶意）能为协作学习提供强大的鲁棒性保障。