AI Glossary

Federated Learning

A machine learning approach that allows models to learn from distributed data without requiring all data to be collected in one location.

Overview

Traditional machine learning often requires data to be collected and stored in a central location before training begins.

In some situations, this may create privacy, security, or regulatory concerns.

Federated learning offers an alternative approach.

Federated learning allows AI models to learn from data distributed across multiple devices or locations without requiring all data to be centralized.

A helpful way to think about federated learning is a group project.

Instead of everyone sending their work to one person, each participant contributes insights while keeping their materials locally.

Similarly, federated learning allows learning to occur across distributed environments while reducing the need to move sensitive data.

This approach is increasingly discussed in healthcare, finance, mobile applications, and privacy-focused AI environments.

As organizations look for ways to balance innovation and privacy, federated learning continues gaining attention.

Federated learning supports AI development while helping reduce data-sharing requirements.

A smartphone manufacturer may improve predictive features across millions of devices without collecting all user data into a central repository.