Federated Learning & Privacy: Decentralized AI Training | AI with Skakun

Federated Learning & Privacy: Decentralized AI Training

Federated learning represents a paradigm shift in machine learning, enabling model training across distributed data sources while preserving privacy and data sovereignty. This comprehensive guide explores the principles, challenges, and applications of federated learning in building privacy-preserving AI systems.

What is Federated Learning?

Privacy-Preserving Techniques

1. Differential Privacy

Adding calibrated noise to model updates to prevent inference attacks.

Membership Inference: Determining if data was used in training

Property Inference: Inferring properties of training data

Federated Learning Algorithms

Frequently asked questions