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：协作学习技术有望训练出优于基于单一实体数据训练的机器学习模型。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者，例如每家公司都希望通过提供最佳推荐来吸引客户。这可能会激励不诚实的更新，损害其他参与者的模型，从而可能削弱协作的益处。在本工作中，我们建立了一个博弈模型来模拟此类交互，并在该框架内研究了两个学习任务：单轮均值估计和多轮基于强凸目标的随机梯度下降。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与完全协作相当的方法。最后，我们在标准的非凸联邦学习基准上实证展示了我们激励机制的有效性。我们的研究表明，明确建模不诚实客户的动机和行为（而非假定其恶意）可以为协作学习提供强大的鲁棒性保障。