26 citations · 30 across the 6 of their papers we have counts for
4 papers · 1 filter
Energy and Memory-Efficient Federated Learning With Ordered Layer Freezing
Ziru Niu, Hai Dong, A. K. Qin +2
Federated Learning (FL) has emerged as a privacy-preserving paradigm for training machine learning models across distributed edge devices in the Internet of Things (IoT). By keepin…
Bridging Generalization Gap of Heterogeneous Federated Clients Using Generative Models
Ziru Niu, Hai Dong, A. K. Qin
Federated Learning (FL) is a privacy-preserving machine learning framework facilitating collaborative training across distributed clients. However, its performance is often comprom…
FedSPU: Personalized Federated Learning for Resource-constrained Devices with Stochastic Parameter Update
Ziru Niu, Hai Dong, A. K. Qin
Personalized Federated Learning (PFL) is widely employed in IoT applications to handle high-volume, non-iid client data while ensuring data privacy. However, heterogeneous edge dev…
FLrce: Resource-Efficient Federated Learning with Early-Stopping Strategy
Ziru Niu, Hai Dong, A. Kai Qin +1
Federated Learning (FL) achieves great popularity in the Internet of Things (IoT) as a powerful interface to offer intelligent services to customers while maintaining data privacy.…