Big Data Concept

  Introduction Imagine harnessing the power of machine learning without compromising sensitive data. In privacy-sensitive industries like healthcare, the need for data security and confidentiality is paramount. Enter federated learning—a revolutionary approach to decentralized big data analytics. According to a report by McKinsey, federated learning could significantly enhance data privacy while enabling robust machine learning across distributed data sources. This article explores how federated learning works, its benefits, and its critical role in privacy-sensitive industries like healthcare. Body Section 1: Background and Context Understanding Federated Learning: Federated learning is a machine learning technique that allows models to be trained across multiple decentralized devices or servers holding local data samples, without exchanging the data itself. Instead of centralizing data, federated learning brings the model to the data source. The model is trained locally on each d...