Communication-Efficient Federated Learning for Wireless Edge Intelligence in IoT

Abstract

The rapidly expanding number of IoT devices is generating huge quantities of data, but public concern over data privacy means users are apprehensive to send data to a central server for Machine Learning (ML) purposes. The easilychanged behaviours of edge infrastructure that Software Defined Networking provides makes it possible to collate IoT data at edge servers and gateways, where Federated Learning (FL) can be performed: building a central model without uploading data to the server. FedAvg is a FL algorithm which has been the subject of much study, however it suffers from a large number of rounds to convergence with non-Independent, Identically Distributed (non-IID) client datasets and high communication costs per round. We propose adapting FedAvg to use a distributed form of Adam optimisation, greatly reducing the number of rounds to convergence, along with novel compression techniques, to produce Communication-Efficient FedAvg (CE-FedAvg). We perform extensive experiments with the MNIST/CIFAR-10 datasets, IID/non-IID client data, varying numbers of clients, client participation rates, and compression rates. These show CE-FedAvg can converge to a target accuracy in up to 6× less rounds than similarly compressed FedAvg, while uploading up to 3× less data, and is more robust to aggressive compression. Experiments on an edge-computing-like testbed using Raspberry Pi clients also show CE-FedAvg is able to reach a target accuracy in up to 1.7× less real time than FedAvg.