Cited by 436Privacy-Preserving Technologies in DataIoT and Edge/Fog ComputingStochastic Gradient Optimization Techniques

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Communication-Efficient Federated Learning for Wireless Edge Intelligence in IoT

Mills, Jed, Hu, Jia, Min, Geyong·2020·IEEE Internet of Things Journal

DOI 10.1109/jiot.2019.2956615

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Abstract

The rapidly expanding number of Internet of Things (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 easily changed behaviors of edge infrastructure that software-defined networking (SDN) 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 an 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 data sets and high communication costs per round. We propose adapting FedAvg to use a distributed form of Adam optimization, greatly reducing the number of rounds to convergence, along with the novel compression techniques, to produce communication-efficient FedAvg (CE-FedAvg). We perform extensive experiments with the MNIST/CIFAR-10 data sets, IID/non-IID client data, varying numbers of clients, client participation rates, and compression rates. These show that 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 that CE-FedAvg is able to reach a target accuracy in up to 1.7× less real time than FedAvg.

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Authors: Mills, J., Hu, J., & Min, G.
Affiliations: University of Exeter (GB)
ORCID: Mills, Jed
Hu, Jia
Min, Geyong
Venue: IEEE Internet of Things Journal
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2020-07
Volume: 7
Issue: 7
Pages: 5986-5994
ISSN: 2327-4662 (ISSN-L)
2372-2541
DOI: 10.1109/jiot.2019.2956615
OpenAlex: W2991236681
Cited by: 436
References: 27
Citations by year: 2026: 28 · 2025: 50 · 2024: 88 · 2023: 89 · 2022: 96 · 2021: 56 · 2020: 29
FWCI: 20.88
Type: journal article
Language: en
Open access: closed
Licence: ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
Locations: IEEE Internet of Things Journal (publishedVersion)
Open Research Exeter (University of Exeter) (acceptedVersion)
Figshare (submittedVersion)
Funders: EPSRC DTP Studentship
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
Physical Sciences › Computer Science › Artificial Intelligence › Stochastic Gradient Optimization Techniques
Keywords: federated learning, communication efficiency, Internet of Things, FedAvg, data compression, non-IID data, edge computing, data privacy, software-defined networking

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Communication-Efficient Federated Learning for Wireless Edge Intelligence in IoT

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