Authors: Rutik Jadyar, Hritik Acharya, Dr. Jasbir Kaur, Assistant Professor Ms. Ifra kampoo
Abstract: In recent years, the use of digital health data has grown rapidly. However, sharing sensitive medical information can lead to serious privacy concerns. Traditional data analysis methods require centralizing data, which poses a risk of exposing private information. Federated Learning (FL) is a new method that allows hospitals and healthcare institutions to collaborate on machine learning models without sharing actual patient data. Instead, the model is trained across different devices or servers holding local data. This paper explains how FL works, its benefits for healthcare, and how it can be applied to protect patient privacy while still enabling powerful data analysis.
