Incentives for Federated Learning: a Hypothesis Elicitation Approach

When a company relies on distributed users' data to train a machine learning model, federated learning [37, 54, 25] promotes the idea that users/customers' data should be kept local, and only the locally held/learned hypothesis will be shared/contributed from each user. While federated learning has observed success in keyboard recognition [21] and in language modeling [8], existing works have made an implicit assumption that participating users will be willing to contribute their local hypotheses to help the central entity to refine the model. Nonetheless, without proper incentives, agents can choose to opt out of the participation, to contribute either uninformative or outdated information, or to even contribute malicious model information. Though being an important question for federated learning [54, 34, 55, 56], this capability of providing adequate incentives for user participation has largely been overlooked. In this paper we ask the questions that: Can a machine learning hypothesis be incentivized/elicited by a certain form of scoring rules from self-interested agents? The availability of a scoring rule will help us design a payment for the elicited hypothesis properly to motivate the reporting of high-quality ones. The corresponding solutions complement the literature of federated learning by offering a generic template for incentivizing users' participation.