Secure Federated Learning

Federated learning enables multiple data
owners to collaboratively train robust ML
models without transferring large or sensitive local datasets by only sharing the parameters
of the locally trained models. The Advanced Privacy-Preserving Federated Learning
(APPFL) framework streamlines end-to-end secure and reliable federated learning experiments across resources.