Federated Meta-Learning is a method where multiple computers work together to create a learnable model without having to share their data with each other.
Imagine many smartphones learning together how to solve a task better, but each device keeps its personal data private.
Not only is a model trained, but the system also learns how to quickly adapt to new tasks on each device.
This way, all devices benefit from the shared knowledge without sensitive information being exchanged.