A Comprehensive Review on Federated Learning Recent Advances and Applications
Selvani Deepthi, Priya B.R -, Ayswariya V.J -, Goutham Krishna L.U - · International Journal For Multidisciplinary Research · 2025
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.