Salama, A. orcid.org/0000-0002-3339-8292, Zaidi, S.A., McLernon, D. orcid.org/0000-0001-8278-6171 et al. (1 more author) (2023) FLCC: Efficient Distributed Federated Learning on IoMT over CSMA/CA. In: 2023 IEEE 97th Vehicular Technology Conference (VTC2023-Spring). 2023 IEEE 97th Vehicular Technology Conference (VTC2023-Spring), 20-23 Jun 2023, Florence. IEEE ISBN 979-8-3503-1115-0
Abstract
Federated Learning (FL) has emerged as a promising approach for privacy preservation, allowing sharing of the model parameters between users and the cloud server rather than the raw local data. FL approaches have been adopted as a cornerstone of distributed machine learning (ML) to solve several complex use cases. FL presents an interesting interplay between communication and ML performance when implemented over distributed wireless nodes. Both the dynamics of networking and learning play an important role. In this article, we investigate the performance of FL on an application that might be used to improve a remote healthcare system over ad hoc networks which employ CSMA/CA to schedule its transmissions. Our FL over CSMA/CA (FLCC) model is designed to eliminate untrusted devices and harness frequency reuse and spatial clustering techniques to improve the throughput required for coordinating a distributed implementation of FL in the wireless network.In our proposed model, frequency allocation is performed on the basis of spatial clustering performed using virtual cells. Each cell assigns an FL server and dedicated carrier frequencies to exchange the updated model's parameters within the cell. We present two metrics to evaluate the network performance: 1) probability of successful transmission while minimizing the interference, and 2) performance of distributed FL model in terms of accuracy and loss while considering the networking dynamics.We benchmark the proposed approach using a well-known MNIST dataset for performance evaluation. We demonstrate that the proposed approach outperforms the baseline FL algorithms in terms of explicitly defining the chosen users' criteria and achieving high accuracy in a robust network.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Keywords: | Federated Learning; CSMA/CA; IoT Privacy |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) > Robotics, Autonomous Systems & Sensing (Leeds) The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) |
Funding Information: | Funder Grant number Royal Academy of Engineering Not Known |
Depositing User: | Symplectic Publications |
Date Deposited: | 04 Jan 2024 11:49 |
Last Modified: | 09 Jan 2024 14:50 |
Published Version: | https://ieeexplore.ieee.org/document/10200294 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/vtc2023-spring57618.2023.10200294 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:206995 |