Abstract
The research project called Quantifying Uncertainty in Adversarial Federated Learning aims to analyze and propose new approaches to distributed machine learning models that maintain privacy and security restrictions. Federated Learning (FL) is a promising approach to training data collaboratively on distributed devices while accounting for privacy restrictions. However, the FL training pr…