  To strengthen data privacy and security, federated learning as an emerging
machine learning technique is proposed to enable large-scale nodes, e.g.,
mobile devices, to distributedly train and globally share models without
revealing their local data. This technique can not only significantly improve
privacy protection for mobile devices, but also ensure good performance of the
trained results collectively. Currently, most the existing studies focus on
optimizing federated learning algorithms to improve model training performance.
However, incentive mechanisms to motivate the mobile devices to join model
training have been largely overlooked. The mobile devices suffer from
considerable overhead in terms of computation and communication during the
federated model training process. Without well-designed incentive,
self-interested mobile devices will be unwilling to join federated learning
tasks, which hinders the adoption of federated learning. To bridge this gap, in
this paper, we adopt the contract theory to design an effective incentive
mechanism for simulating the mobile devices with high-quality (i.e.,
high-accuracy) data to participate in federated learning. Numerical results
demonstrate that the proposed mechanism is efficient for federated learning
with improved learning accuracy.

Machine Learning;Computer Science and Game Theory;Networking and Internet Architecture