**Federated Learning Basics: A Breakthrough in Decentralized

Federated Learning Basics: A Breakthrough in Decentralized AI Training

Federated learning is a revolutionary approach to machine learning (ML) that enables the collaborative training of AI models across multiple decentralized devices or organizations without sharing sensitive data. This breakthrough technology has far-reaching implications for data privacy, security, and efficiency.

Key Concepts:

1. Decentralized Data: In traditional ML, data is centralized on a single server, which can be a single point of failure for data security. Federated learning, on the other hand, involves training AI models on decentralized devices or organizations, reducing the risk of data breaches.
2. Client-Server Architecture: Federated learning employs a client-server architecture, where clients (devices or organizations) collaborate with a central server to train the AI model. Clients send model updates to the server, which aggregates the updates without accessing raw data.
3. **Ag...

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