Federated learning (FL) is a machine learning paradigm where multiple client devices train models collaboratively without data exchange.

Certifiablelocalrobustness,which rigorouslyprecludes small-normadversarial examples, has received significant attention as a means of addressing security concerns in deep learning.

When deploying machine learning estimators in science and engineering (SAE) domains, it is critical to avoid failed estimations that can have disastrous consequences, e.g., in aero engine design.