20 citations · 59 across the 26 of their papers we have counts for
11 papers · 1 filter
Adversarial Federated Consensus Learning for Surface Defect Classification Under Data Heterogeneity in IIoT
Jixuan Cui, Jun Li, Zhen Mei +3
The challenge of data scarcity hinders the application of deep learning in industrial surface defect classification (SDC), as it's difficult to collect and centralize sufficient tr…
Resource Efficient Asynchronous Federated Learning for Digital Twin Empowered IoT Network
Shunfeng Chu, Jun Li, Jianxin Wang +4
As an emerging technology, digital twin (DT) can provide real-time status and dynamic topology mapping for Internet of Things (IoT) devices. However, DT and its implementation with…
Blockchain-aided wireless federated learning: Resource allocation and client scheduling
Jun Li, Weiwei Zhang, Kang Wei +4
Federated learning (FL) based on the centralized design faces both challenges regarding the trust issue and a single point of failure. To alleviate these issues, blockchain-aided d…
Trustworthy DNN Partition for Blockchain-enabled Digital Twin in Wireless IIoT Networks
Xiumei Deng, Jun Li, Long Shi +5
Digital twin (DT) has emerged as a promising solution to enhance manufacturing efficiency in industrial Internet of Things (IIoT) networks. To promote the efficiency and trustworth…
Robust Model Aggregation for Heterogeneous Federated Learning: Analysis and Optimizations
Yumeng Shao, Jun Li, Long Shi +6
Conventional synchronous federated learning (SFL) frameworks suffer from performance degradation in heterogeneous systems due to imbalanced local data size and diverse computing po…
Deploying Graph Neural Networks in Wireless Networks: A Link Stability Viewpoint
Jun Li, Weiwei Zhang, Kang Wei +3
As an emerging artificial intelligence technology, graph neural networks (GNNs) have exhibited promising performance across a wide range of graph-related applications. However, inf…
Towards Communication-efficient Federated Learning via Sparse and Aligned Adaptive Optimization
Xiumei Deng, Jun Li, Kang Wei +6
Adaptive moment estimation (Adam), as a Stochastic Gradient Descent (SGD) variant, has gained widespread popularity in federated learning (FL) due to its fast convergence. However,…
Industrial Metaverse: Enabling Technologies, Open Problems, and Future Trends
Shiying Zhang, Jun Li, Long Shi +4
As an emerging technology that enables seamless integration between the physical and virtual worlds, the Metaverse has great potential to be deployed in the industrial production f…
Communication-Efficient Model Aggregation with Layer Divergence Feedback in Federated Learning
Liwei Wang, Jun Li, Wen Chen +2
Federated Learning (FL) facilitates collaborative machine learning by training models on local datasets, and subsequently aggregating these local models at a central server. Howeve…
Decision Transformers for Wireless Communications: A New Paradigm of Resource Management
Jie Zhang, Jun Li, Long Shi +4
As the next generation of mobile systems evolves, artificial intelligence (AI) is expected to deeply integrate with wireless communications for resource management in variable envi…
Energy-Efficient Wireless Federated Learning via Doubly Adaptive Quantization
Xuefeng Han, Wen Chen, Jun Li +5
Federated learning (FL) has been recognized as a viable distributed learning paradigm for training a machine learning model across distributed clients without uploading raw data. H…