A survey on federated learning: evolution, applications and challenges

Lingyu Shi

doi:10.54254/2755-2721/22/20231177

Research Article

Open access

Published on 23 October 2023

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Shi,L. (2023). A survey on federated learning: evolution, applications and challenges. Applied and Computational Engineering,22,106-111.

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A survey on federated learning: evolution, applications and challenges

Lingyu Shi *^,1,

¹ Wuhan Britain China School

* Author to whom correspondence should be addressed.

https://doi.org/10.54254/2755-2721/22/20231177

Abstract

Federated learning, a machine learning technique that enables collaborative model training on decentralized data, has gained significant attention in recent years due to its potential to address privacy concerns. This paper explores the evolution, applications, and challenges of federated learning. The research topic focuses on providing a comprehensive understanding of federated learning, its advantages, and limitations. The purpose of the study is to highlight the importance of federated learning in preserving data privacy and enabling collaborative model training. The study conducted a literature review by systematically analyzing relevant papers from peer-reviewed journals, conference proceedings, and reputable sources. The results reveal that federated learning offers a promising solution for collaborative machine learning while addressing concerns related to data privacy and security. The study emphasizes the need for further research in optimizing communication protocols, scalability, and privacy-preserving techniques. Overall, this paper contributes to the understanding of federated learning and its potential for secure and efficient decentralized learning paradigms.

Keywords

federated learning, decentralized data, privacy preservation, collaborative model training, model aggregation

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References

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Cite this article

Shi,L. (2023). A survey on federated learning: evolution, applications and challenges. Applied and Computational Engineering,22,106-111.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 5th International Conference on Computing and Data Science

Conference website: https://2023.confcds.org/

ISBN：978-1-83558-035-6(Print) / 978-1-83558-036-3(Online)

Conference date: 14 July 2023

Editor：Alan Wang, Marwan Omar, Roman Bauer

Series: Applied and Computational Engineering

Volume number: Vol.22

ISSN：2755-2721(Print) / 2755-273X(Online)

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