Explore how FedNova, FedAvg, and FedProx balance privacy and accuracy in federated learning. Discover where noise injection best preserves model performance under differential privacy.
Overview
A benchmarking system that tests federated learning algorithms FedNova, FedAvg, and FedProx under differential privacy constraints to find the optimal balance between model accuracy and data privacy.
