Using enclaves against adversarial attacks in federated learning – How to drive safely your Tesla against road-warriors?
Federated learning (FL) is an increasingly popular decentralized machine learning (ML) paradigm. FL client devices share with a trusted server only their local individual updates of a given ML model held in memory by all clients, rather than the data used to train it. These updates are called gradients, and are produced when clients run the ML model on their data locally.
