  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.
