Federated Learning

Federated Learning in Production: Challenges, Defenses, and Real-World Deployments

Federated learning introduces engineering challenges that don't exist in centralized training: statistical heterogeneity across clients, communication efficiency, adversarial clients, and privacy attack vectors. Production deployments require solving all of these simultaneously while maintaining model quality at scale.

What is Federated Learning in Machine Learning?

Federated learning trains machine learning models across many decentralized devices without ever moving raw data to a central server. Only model updates — gradients — are shared and aggregated. This enables AI to learn from sensitive, distributed data while preserving privacy at the source.