Title: Machine and federated learning for high-performance computing: a survey

Authors: Akshat Gaurav; Konstantinos E. Psannis

Addresses: Ronin Institute, Montclair, New Jersey 07043, USA ' University of Macedonia, Egnatia 156, Thessaloniki 546 36, Greece

Abstract: A paradigm shift in machine learning (ML) application models has occurred in the preceding years due to privacy and deep learning aspirations. The recently created decentralised paradigm of ML is known as federated learning. Federated learning (FL) is a ML technique in which many dispersed nodes use their locally stored data to train a common prediction model. Better data privacy is possible because the training data is not routed to a central server. FL offloads the processed information to the server and does not need clients to share their personal information. However, FL is a new field that has yet to achieve mainstream acceptance and is still in the development phase. In this context, the purpose of our study is to provide a more comprehensive overview of the most important protocols, platforms, and real-world FL use cases, which will allow researchers to develop privacy-preserving solutions for businesses that require FL.

Keywords: federated learning; blockchain; machine learning; internet of things; IoT.

DOI: 10.1504/IJHPCN.2021.120750

International Journal of High Performance Computing and Networking, 2021 Vol.17 No.1, pp.28 - 38

Received: 06 Jul 2021
Accepted: 23 Jul 2021
Published online: 07 Feb 2022 *

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