Yes indeed. Using federated learning with Flower makes training over multiple disconnected partitions possible. Additionally there are privacy enhancing techniques such as differential privacy but they come with a cost and are not always nesesecary.

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.