Federated Learning Hub

What is Federated Learning?

Federated Learning (FL) is a machine learning technique that trains an algorithm across multiple decentralized parties without exchanging the raw data. This approach is essential when data privacy, security, or sovereignty is a primary concern.

In this scenario, we have two parties: Team A (the technical ML experts) and Team B (the owners of the valuable "body of knowledge," or data). The core principle is to move the model to the data, not the other way around.

Core Principle: Model-to-Data

Traditional Approach (Data-to-Model)

Team B's Data

Central Server (Team A)

Data privacy is lost as raw data is shared.

Federated Approach (Model-to-Data)

Global Model (from A)

Team B's Secure Data

Data never leaves Team B's environment.