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Federated learning (FL) is an emerging machine learning technique thataggregates model attributes from a large number of distributed devices. Severalunique features such as energy saving and privacy preserving make FL a highlypromising learning approach for power-limited and privacy sensitive devices.Although distributed computing can lower down the information amount that needsto be uploaded, model updates in FL can still experience performancebottleneck, especially for updates via wireless connections. In this work, weinvestigate the performance of FL update with mobile edge devices that areconnected to the parameter server (PS) with practical wireless links, whereuplink update from user to PS has very limited capacity. Different from theexisting works, we apply non-orthogonal multiple access (NOMA) together withgradient compression in the wireless uplink. Simulation results show that ourproposed scheme can significantly reduce aggregation latency while achievingsimilar accuracy.

Adaptive Federated Learning With Gradient Compression in Uplink NOMA