H-FedSN: Hierarchical Federated Learning with Sparse Networks for IoT Applications

Abstract

H-FedSN pushes the boundaries of IoT with a unique approach that uses masking techniques to train a sparse network, enhancing personalization through client-based transfer learning. Applied to non-IID IoT datasets, it achieves high accuracy and boosts communication efficiency by at least 58x compared to traditional federated learning approaches.