``` Federated learning (also known as collaborative learning) is a machine learning technique that trains an algorithm across multiple decentralized edge devices or servers holding local data samples, without exchanging them. ```
basically you can train ML models collaboratively without ever seeing the other datasets. One example would be multiple hospitals training models to detect breast cancer without the need to exchange the data samples.
Another example is how Google trains models for the keyboard on Android. See here: https://ai.googleblog.com/2017/04/federated-learning-collabo...
You can train models over multiple silos, devices, users and many other kind of partitioning where for some reason you can't aggregate the dataset centrally.
