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A federated machine learning protocol for fog networks

Foukalas, F; Tziouvaras, A; (2021) A federated machine learning protocol for fog networks. In: Proceedings of the IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS). IEEE: Vancouver, BC, Canada. Green open access

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Abstract

In this paper, we present a federated learning (FL) protocol for fog networking applications. The fog networking architecture is compatible with the Internet of Things (IoT) edge computing concept of the Internet Engineering Task Force (IETF). The FL protocol is designed and specified for constrained IoT devices extended to the cloud through the edge. The proposed distributed edge intelligence solution is tested through experimental trials for specific application scenarios. The results depicts the performance of the proposed FL protocol in terms of accuracy of the intelligence and latency of the messaging. Next generation Internet will rely on such protocols, which can deploy edge intelligence more efficient to the extreme amount of newly connected IoT devices.

Type: Proceedings paper
Title: A federated machine learning protocol for fog networks
Event: IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)
ISBN-13: 9781665404433
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/INFOCOMWKSHPS51825.2021.9484485
Publisher version: http://dx.doi.org/10.1109/INFOCOMWKSHPS51825.2021....
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Performance evaluation , Protocols, Conferences, Force, Machine learning, Computer architecture, Internet of Things
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Brain Repair and Rehabilitation
URI: https://discovery.ucl.ac.uk/id/eprint/10135434
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