Visible to the public Age-Based Scheduling Policy for Federated Learning in Mobile Edge Networks

TitleAge-Based Scheduling Policy for Federated Learning in Mobile Edge Networks
Publication TypeConference Paper
Year of Publication2020
AuthorsYang, Howard H., Arafa, Ahmed, Quek, Tony Q. S., Vincent Poor, H.
Conference NameICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Keywordsage-of-update, Collaborative Work, federated learning, Human Behavior, Measurement, Metrics, Mobile Edge Computing, Privacy Policies, pubcrawl, Scalability, scheduling policy, Servers, Signal processing, Signal processing algorithms, speech processing, Training
AbstractFederated learning (FL) is a machine learning model that preserves data privacy in the training process. Specifically, FL brings the model directly to the user equipments (UEs) for local training, where an edge server periodically collects the trained parameters to produce an improved model and sends it back to the UEs. However, since communication usually occurs through a limited spectrum, only a portion of the UEs can update their parameters upon each global aggregation. As such, new scheduling algorithms have to be engineered to facilitate the full implementation of FL. In this paper, based on a metric termed the age of update (AoU), we propose a scheduling policy by jointly accounting for the staleness of the received parameters and the instantaneous channel qualities to improve the running efficiency of FL. The proposed algorithm has low complexity and its effectiveness is demonstrated by Monte Carlo simulations.
Citation Keyyang_age-based_2020