research-article

Federated-Learning-Aided Next-Generation Edge Networks for Intelligent Services

Authors:

Abhishek Hazra,

Mainak Adhikari,

Sudarshan Nandy,

Khushbu Doulani,

Varun G MenonAuthors Info & Claims

IEEE Network, Volume 36, Issue 3

Pages 56 - 64

https://doi.org/10.1109/MNET.007.2100549

Published: 01 May 2022 Publication History

Abstract

Nowadays, federated learning (F1) has been proposed as an emerging technology to store sensory data and train the edge networks using a set of computing devices with minimum training time. However, a collection of heterogeneous participating devices with different processing power and energy usage are used to analyze the model parameters locally. Therefore, an intelligent service provisioning mechanism with the F1 technique needs to be developed at the edge networks. This strategy can increase the security and privacy of the network while minimizing the training time on resource-constrained edge devices. In this magazine, we describe the importance of the FL-aided hybrid edge intelligent framework for next-generation Internet of Things applications. Moreover, to enhance the critical service provisioning functionality, we highlight two use case studies along with their potential research directions, including intelligent transportation systems and intelligent healthcare systems. Finally, this work concludes with a set of potential future research directions of FL-aided edge networks.

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Cited By

Devi R(2024)IPSHO-Fed: a hybrid federated learning and spotted hyena optimization approach for trust assessmentNeural Computing and Applications10.1007/s00521-023-09330-136:10(5571-5594)Online publication date: 1-Apr-2024
https://dl.acm.org/doi/10.1007/s00521-023-09330-1
Zhang SZhang SYuan WLi YHanzo L(2023)Efficient Rate-Splitting Multiple Access for the Internet of Vehicles: Federated Edge Learning and Latency MinimizationIEEE Journal on Selected Areas in Communications10.1109/JSAC.2023.324071641:5(1468-1483)Online publication date: 1-May-2023
https://dl.acm.org/doi/10.1109/JSAC.2023.3240716

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Federated-Learning-Aided Next-Generation Edge Networks for Intelligent Services

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Published: 01 May 2022

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Devi R(2024)IPSHO-Fed: a hybrid federated learning and spotted hyena optimization approach for trust assessmentNeural Computing and Applications10.1007/s00521-023-09330-136:10(5571-5594)Online publication date: 1-Apr-2024
https://dl.acm.org/doi/10.1007/s00521-023-09330-1
Zhang SZhang SYuan WLi YHanzo L(2023)Efficient Rate-Splitting Multiple Access for the Internet of Vehicles: Federated Edge Learning and Latency MinimizationIEEE Journal on Selected Areas in Communications10.1109/JSAC.2023.324071641:5(1468-1483)Online publication date: 1-May-2023
https://dl.acm.org/doi/10.1109/JSAC.2023.3240716

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