A Comprehensive Review on Federated Learning Recent Advances and Applications

Author(s)	Ms. Deepthi Rani S S, Priya B.R, Ayswariya V.J, Goutham Krishna L.U
Country	India
Abstract	Abstract: Federated learning (FL) is a machine learning setting where many clients collaboratively train a model under the orchestration of a central server, while keeping the training data decentralized. FL embodies the principles of focused data collection and minimization, and can mitigate many of the systemic privacy risks and costs resulting from traditional, centralized machine learning and data science approaches. The healthcare industry is one of the most vulnerable to cybercrime and privacy violations because health data is very sensitive and spread out in many places. Recent confidentiality trends and a rising number of infringements in different sectors make it crucial to implement new methods that protect data privacy while maintaining accuracy and sustainability.
Keywords	Keywords: Federated Learning, intelligent Intrusion Detection and Prevention Systems (IDS/IPS), homomorphism encryption.
Field	Computer Applications
Published In	Volume 7, Issue 3, May-June 2025
Published On	2025-05-21
DOI	https://doi.org/10.36948/ijfmr.2025.v07i03.45186
Short DOI	https://doi.org/g9mh6r

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International Journal For Multidisciplinary Research