Hierarchical Federated Learning with Hierarchical Differential Privacy: Balancing Privacy, Performance, and Resource Utilization

Hierarchical Federated Learning with Hierarchical Differential Privacy (H2FDP) is a methodology that jointly optimizes privacy and performance in hierarchical networks by adapting differential privacy noise injection at different layers of the established federated learning hierarchy.