Advancements and Challenges in Federated Learning: General Approaches and Methods

Authors

  • S.K.Umamaheswari,K.K.Baalaji

Keywords:

Federated Learning (FL), Decentralized Machine Learning, Privacy-Preserving Techniques, Communication Efficiency, Energy-Efficient FL, Distributed Optimization

Abstract

Federated Learning (FL) has turned out to be theshifted thinking in distributed ML that helps to overcome difficulties in organization privacy, data safety, and computationpower. The aim of this review paper is to present newdevelopments in the FL algorithms

References

Lin, Frank Po-Chen, et al. "Semi-decentralized federated learning with cooperative D2D local model aggregations." IEEE Journal on

Selected Areas in Communications 39.12 (2021): 3851-3869.

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Published

2024-01-15

How to Cite

S.K.Umamaheswari,K.K.Baalaji. (2024). Advancements and Challenges in Federated Learning: General Approaches and Methods. Journal of Computational Analysis and Applications (JoCAAA), 32(1), 1202–1214. Retrieved from https://www.eudoxuspress.com/index.php/pub/article/view/4420

Issue

Vol. 32 No. 1 (2024): JOCAAA

Section

Articles

License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.