“Federated Learning – Hope and Scope” explores the concept of federated learning (FL) as a decentralized approach to machine learning. It addresses challenges of data privacy and security, particularly in sectors like smart health and fintech. The document explains how FL enables collaborative training of models across multiple devices or institutions without sharing sensitive data. It discusses different types of FL, such as Horizontal, Vertical, and Transfer Learning, and highlights the potential of FL to enhance data privacy while improving machine learning model accuracy. The study emphasizes FL’s promise in creating privacy-preserving solutions for healthcare and financial services, which are traditionally constrained by data sharing regulations.
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