The DOI matches its Crossref record; 25 of the 27 references it cites were found in a registry or an index.
Communication-Efficient Federated Learning for Wireless Edge Intelligence in IoT
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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In the citing paper
“Implementation results on an industrial edge computing-like testbed using Raspberry Pi clients show the effectiveness of the proposed scheme with lower communication latency while the training accuracy is preserved.”
From Federated Learning for Internet of Things: A Comprehensive Survey (2021) · cites this paper as [174]
In the citing paper
“Furthermore, to enable intelligent and privacy-enhanced edgebased industrial IoT applications, many efficient FL solutions have been introduced to solve communication issues by using update reconstruction techniques [174], [175] or relevance detection during the model update [177].”
From Federated Learning for Internet of Things: A Comprehensive Survey (2021) · cites this paper as [174]
Citations
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Metadata
- Authors
- Mills, J., Hu, J., & Min, G.
- Affiliations
- University of Exeter (GB)
- Venue
- IEEE Internet of Things Journal
- Published
- 2020-07
- Volume
- 7
- Issue
- 7
- Pages
- 5986-5994
- ISSN
- 2327-4662 (ISSN-L)
- 2372-2541
- 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
- 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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