54 citations · 60 across the 15 of their papers we have counts for
6 papers · 1 filter
HFedMS: Heterogeneous Federated Learning with Memorable Data Semantics in Industrial Metaverse
Shenglai Zeng, Zonghang Li, Hongfang Yu +4
Federated Learning (FL), as a rapidly evolving privacy-preserving collaborative machine learning paradigm, is a promising approach to enable edge intelligence in the emerging Indus…
PerFED-GAN: Personalized Federated Learning via Generative Adversarial Networks
Xingjian Cao, Gang Sun, Hongfang Yu +1
Federated learning is gaining popularity as a distributed machine learning method that can be used to deploy AI-dependent IoT applications while protecting client data privacy and…
Data Heterogeneity-Robust Federated Learning via Group Client Selection in Industrial IoT
Zonghang Li, Yihong He, Hongfang Yu +4
Nowadays, the industrial Internet of Things (IIoT) has played an integral role in Industry 4.0 and produced massive amounts of data for industrial intelligence. These data locate o…
Cross-Silo Heterogeneous Model Federated Multitask Learning
Xingjian Cao, Zonghang Li, Gang Sun +2
Federated learning (FL) is a machine learning technique that enables participants to collaboratively train high-quality models without exchanging their private data. Participants u…
Heterogeneous Federated Learning via Grouped Sequential-to-Parallel Training
Shenglai Zeng, Zonghang Li, Hongfang Yu +4
Federated learning (FL) is a rapidly growing privacy-preserving collaborative machine learning paradigm. In practical FL applications, local data from each data silo reflect local…
Compacting Deep Neural Networks for Internet of Things: Methods and Applications
Ke Zhang, Hanbo Ying, Hong-Ning Dai +4
Deep Neural Networks (DNNs) have shown great success in completing complex tasks. However, DNNs inevitably bring high computational cost and storage consumption due to the complexi…