Federated Learning: Overview, Strategies, Applications, Tools and Future Directions

Authors

Betul Yurdem, Izmir Bakircay University
Murat Kuzlu, Old Dominion University
Mehmet Kemal Gullu, Izmir Bakircay University
Maliha Tabassum, Old Dominion UniversityFollow

Document Type

Article

Publication Date

2024

DOI

10.1016/j.heliyon.2024.e38137

Publication Title

Heliyon

Volume

10

Issue

19

Pages

e38137 (1-24)

Abstract

Federated learning (FL) is a distributed machine learning process, which allows multiple nodes to work together to train a shared model without exchanging raw data. It offers several key advantages, such as data privacy, security, efficiency, and scalability, by keeping data local and only exchanging model updates through the communication network. This review paper provides a comprehensive overview of federated learning, including its principles, strategies, applications, and tools along with opportunities, challenges, and future research directions. The findings of this paper emphasize that federated learning strategies can significantly help overcome privacy and confidentiality concerns, particularly for high-risk applications.

Rights

© The Authors.

This is an open access article under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.

Data Availability

Article states: "Not applicable."

Original Publication Citation

Yurdem, B., Kuzlu, M., Gullu, M. K., Catak, F. O., & Tabassum, M. (2024). Federated learning: Overview, strategies, applications, tools and future directions. Heliyon, 10(19), 1-24, Article e38137. https://doi.org/10.1016/j.heliyon.2024.e38137

ORCID

0000-0002-8719-2353 (Kuzlu)

Repository Citation

Yurdem, Betul; Kuzlu, Murat; Gullu, Mehmet Kemal; and Tabassum, Maliha, "Federated Learning: Overview, Strategies, Applications, Tools and Future Directions" (2024). Engineering Technology Faculty Publications. 243.
https://digitalcommons.odu.edu/engtech_fac_pubs/243