  Federated learning provides a promising paradigm for collecting machine
learning models from distributed data sources without compromising users' data
privacy. The success of a credible federated learning system builds on the
assumption that the decentralized and self-interested users will be willing to
participate to contribute their local models in a trustworthy way. However,
without proper incentives, users might simply opt out the contribution cycle,
or will be mis-incentivized to contribute spam/false information. This paper
introduces solutions to incentivize truthful reporting of a local, user-side
machine learning model for federated learning. Our results build on the
literature of information elicitation, but focus on the questions of eliciting
hypothesis (rather than eliciting human predictions). We provide a scoring rule
based framework that incentivizes truthful reporting of local hypotheses at a
Bayesian Nash Equilibrium. We study the market implementation, accuracy as well
as robustness properties of our proposed solution too. We verify the
effectiveness of our methods using MNIST and CIFAR-10 datasets. Particularly we
show that by reporting low-quality hypotheses, users will receive decreasing
scores (rewards, or payments).
