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Lookup NU author(s): Dr Rehmat UllahORCiD
This is the authors' accepted manuscript of an article that has been published in its final definitive form by IEEE, 2022.
For re-use rights please refer to the publisher's terms and conditions.
© 2014 IEEE. Applying federated learning (FL) on Internet of Things (IoT) devices is necessitated by the large volumes of data they produce and growing concerns of data privacy. However, there are three challenges that need to be addressed to make FL efficient: 1) execution on devices with limited computational capabilities; 2) accounting for stragglers due to computational heterogeneity of devices; and 3) adaptation to the changing network bandwidths. This article presents FedAdapt, an adaptive offloading FL framework to mitigate the aforementioned challenges. FedAdapt accelerates local training in computationally constrained devices by leveraging layer offloading of deep neural networks (DNNs) to servers. Furthermore, FedAdapt adopts reinforcement learning (RL)-based optimization and clustering to adaptively identify which layers of the DNN should be offloaded for each individual device on to a server to tackle the challenges of computational heterogeneity and changing network bandwidth. The experimental studies are carried out on a lab-based testbed and it is demonstrated that by offloading a DNN from the device to the server FedAdapt reduces the training time of a typical IoT device by over half compared to classic FL. The training time of extreme stragglers and the overall training time can be reduced by up to 57%. Furthermore, with changing network bandwidth, FedAdapt is demonstrated to reduce the training time by up to 40% when compared to classic FL, without sacrificing accuracy.
Author(s): Wu D, Ullah R, Harvey P, Kilpatrick P, Spence I, Varghese B
Publication type: Article
Publication status: Published
Journal: IEEE Internet of Things Journal
Year: 2022
Volume: 9
Issue: 21
Pages: 20889-20901
Print publication date: 01/11/2022
Online publication date: 19/05/2022
Acceptance date: 13/05/2022
Date deposited: 11/02/2025
ISSN (electronic): 2327-4662
Publisher: IEEE
URL: https://doi.org/10.1109/JIOT.2022.3176469
DOI: 10.1109/JIOT.2022.3176469
ePrints DOI: 10.57711/t7dk-d966
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