TL;DR
A decentralized machine learning framework where models are trained collaboratively across localized devices or servers without ever sharing the underlying raw data.
Federated Learning allows multiple edge devices to collaboratively train a shared model under the coordination of a central server. Each device processes its own local dataset to calculate weight updates, which are then aggregated by the server to improve the global model. This decentralized process ensures that user privacy is maintained since sensitive data never leaves the source device.
Why this matters for your business
It enables secure, privacy-preserving AI development in highly regulated industries like healthcare and finance where data sharing is strictly prohibited.
