Federated Learning: Preserving Privacy in Distributed Machine Learning

#FederatedLearning

• #MachineLearning
• #PrivacyPreserving

Federated Learning has emerged as a revolutionary approach to training machine learning models while preserving data privacy. In this comprehensive guide, we’ll explore the concepts, implementation, and real-world applications of Federated Learning using Python.

What is Federated Learning?

Federated Learning (FL) enables training machine learning models on decentralized data without sharing the raw data between parties. Instead of collecting all data in a central location, the model travels to where the data resides, learns locally, and only shares model updates.

Key Components and Architecture

Central Server (Aggregator)

• Coordinates the training process
• Aggregates model updates from clients
• Distributes the global model

Clients (Edge Devices)

• Hold local private data
• Train the model locally
• Share only model updates

Let’s implement a basic Federated Learning system using Python: