Reliable Federated Learning for Mobile Networks
Jiawen Kang, Zehui Xiong, Dusit Tao Niyato et al.
2020 · IEEE Wireless Communications · 610 citations
Federated learning, as a promising machine learning approach, has emerged to leverage a distributed personalized dataset from a number of nodes, for example, mobile devices, to improve performance while simultaneously providing privacy preservation for mobile users. In 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…
Explore this paper's citation graph on Constellation.