Federated Learning (FL) is a machine learning technique that allows models to be trained across multiple decentralized devices or servers holding local data samples, without exchanging them. This process is highly relevant in scenarios where data privacy is crucial, such as in healthcare, finance, or mobile device applications.
In this blog, we’ll break down the Federated Learning process step-by-step, provide coding examples, and relate it to a real-life scenario for easier understanding.
Introduction to Federated Learning:
Federated Learning is a collaborative machine learning approach that trains an algorithm across multiple decentralized devices or servers while keeping the data on the devices. The central server only receives updates of the model rather than raw data. This approach maintains data privacy and security, making it ideal for sensitive applications like healthcare or finance.
Real-Life Example: Imagine a network of hospitals wanting to train a machine learning model to detect certain diseases. However, due to privacy laws, they can’t share patient data with each other or a central server. Federated Learning enables each hospital to train the model locally on its data. The model updates are then aggregated to form a global model without sharing…
