Age-Based Scheduling Policy for Federated Learning in Mobile Edge Networks

By Howard H. Yang, A. Arafa, Tony Q. S. Quek, Vincent Poor. 2019

186 citations recorded by the source corpus.

Federated 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.

Subfields: Artificial Intelligence, Computer Networks and Communications, Electrical and Electronic Engineering, Optimization & Training, Federated & Distributed Learning

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Citations in the corpus

Federated Learning With Differential Privacy: Algorithms and Performance Analysis (2019, background): “However, FL also poses several key challenges, such as private information leakage, expensive communication costs between servers and clients, and device variability [9]–[15].”
A Comprehensive Survey of Privacy-preserving Federated Learning (2021, background): “Recently, several research studies on FL in mobile edge computing have been proposed [49, 105, 116, 140, 189, 190, 201, 220].”
Federated Learning With Quantized Global Model Updates (2020, background): “Active device selection schemes have been introduced to alleviate signiﬁcant variability in FL systems, where a subset of devices share the resources and participate at each iteration of training [19–23].”