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

10/31/2019

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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 Carol simulations.

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

Scheduling and Aggregation Design for Asynchronous Federated Learning over Wireless Networks

User Scheduling for Federated Learning Through Over-the-Air Computation

Device Scheduling and Update Aggregation Policies for Asynchronous Federated Learning

Federated Learning for Non-IID Data via Client Variance Reduction and Adaptive Server Update

Scheduling Policies for Federated Learning in Wireless Networks

FedDUAP: Federated Learning with Dynamic Update and Adaptive Pruning Using Shared Data on the Server

Multi-Job Intelligent Scheduling with Cross-Device Federated Learning

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

Related Research

Scheduling and Aggregation Design for Asynchronous Federated Learning over Wireless Networks

User Scheduling for Federated Learning Through Over-the-Air Computation

Device Scheduling and Update Aggregation Policies for Asynchronous Federated Learning

Federated Learning for Non-IID Data via Client Variance Reduction and Adaptive Server Update

Scheduling Policies for Federated Learning in Wireless Networks

FedDUAP: Federated Learning with Dynamic Update and Adaptive Pruning Using Shared Data on the Server

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