13 citations · 36 across the 6 of their papers we have counts for
6 papers
Knowledge-Enhanced Semi-Supervised Federated Learning for Aggregating Heterogeneous Lightweight Clients in IoT
Jiaqi Wang, Shenglai Zeng, Zewei Long +3
Federated learning (FL) enables multiple clients to train models collaboratively without sharing local data, which has achieved promising results in different areas, including the…
FedCon: A Contrastive Framework for Federated Semi-Supervised Learning
Zewei Long, Jiaqi Wang, Yaqing Wang +2
Federated Semi-Supervised Learning (FedSSL) has gained rising attention from both academic and industrial researchers, due to its unique characteristics of co-training machine lear…
FedTriNet: A Pseudo Labeling Method with Three Players for Federated Semi-supervised Learning
Liwei Che, Zewei Long, Jiaqi Wang +3
Federated Learning has shown great potentials for the distributed data utilization and privacy protection. Most existing federated learning approaches focus on the supervised setti…
FedSiam: Towards Adaptive Federated Semi-Supervised Learning
Zewei Long, Liwei Che, Yaqing Wang +5
Federated learning (FL) has emerged as an effective technique to co-training machine learning models without actually sharing data and leaking privacy. However, most existing FL me…
Towards Differentially Private Truth Discovery for Crowd Sensing Systems
Yaliang Li, Houping Xiao, Zhan Qin +5
Nowadays, crowd sensing becomes increasingly more popular due to the ubiquitous usage of mobile devices. However, the quality of such human-generated sensory data varies significan…
Developing Synthesis Flows Without Human Knowledge
Cunxi Yu, Houping Xiao, Giovanni De Micheli
Design flows are the explicit combinations of design transformations, primarily involved in synthesis, placement and routing processes, to accomplish the design of Integrated Circu…