Machine and federated learning for high-performance computing: a survey. (3rd February 2022)

• Record Type: Journal Article
• Authors: Gaurav, Akshat; Psannis, Konstantinos E.
• Is Part Of: International journal of high performance computing and networking. Volume 17:Number 1(2021)
• Page Start: 28
• Page End: 38
• Publication Date: 2022-02-03
• Subjects: federated learning -- blockchain -- machine learning -- internet of things -- IoT; High performance computing -- Periodicals; Computer networks -- Periodicals; High performance computing; Periodicals; 004.05
• Languages: English
• ISSNs: 1740-0562

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.