How does federated learning work in AI systems?

Federated learning is a decentralized approach to machine learning where the model is trained across multiple devices or servers holding local data samples, without exchanging them. The process begins with a global model being distributed to these devices, where they each train the model with their own data and only share the model updates with the central server. These updates are then aggregated to improve the global model without ever exposing individual data. This allows for collaborative learning while preserving privacy and security of sensitive information, making federated learning a promising solution for AI systems in industries like healthcare and finance.

This mind map was published on 12 March 2024 and has been viewed 140 times.