  Federated learning, as a promising machine learning approach, has emerged to
leverage a distributed personalized dataset from a number of nodes, e.g.,
mobile devices, to improve performance while simultaneously providing privacy
preservation for mobile users. In the federated learning, training data is
widely distributed and maintained on the mobile devices as workers. A central
aggregator updates a global model by collecting local updates from mobile
devices using their local training data to train the global model in each
iteration. However, unreliable data may be uploaded by the mobile devices
(i.e., workers), leading to frauds in tasks of federated learning. The workers
may perform unreliable updates intentionally, e.g., the data poisoning attack,
or unintentionally, e.g., low-quality data caused by energy constraints or
high-speed mobility. Therefore, finding out trusted and reliable workers in
federated learning tasks becomes critical. In this article, the concept of
reputation is introduced as a metric. Based on this metric, a reliable worker
selection scheme is proposed for federated learning tasks. Consortium
blockchain is leveraged as a decentralized approach for achieving efficient
reputation management of the workers without repudiation and tampering. By
numerical analysis, the proposed approach is demonstrated to improve the
reliability of federated learning tasks in mobile networks.
