20 citations · 59 across the 26 of their papers we have counts for
9 papers · 1 filter
Convergence-Latency-Aware Adaptive Modulation and Resource Allocation in RIS-Assisted Wireless Federated Learning
Liwei Wang, Wen Chen, Jun Li +4
Federated learning (FL) over wireless networks suffers from significant training latency and degraded convergence due to unreliable wireless transmission, especially under blocked…
Hierarchical Federated Learning for Social Network with Mobility
Zeyu Chen, Wen Chen, Jun Li +5
Federated Learning (FL) offers a decentralized solution that allows collaborative local model training and global aggregation, thereby protecting data privacy. In conventional FL f…
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…
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…
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,…
Federated Meta-Learning for Few-Shot Fault Diagnosis with Representation Encoding
Jixuan Cui, Jun Li, Zhen Mei +5
Deep learning-based fault diagnosis (FD) approaches require a large amount of training data, which are difficult to obtain since they are located across different entities. Federat…
Analysis and Optimization of Wireless Federated Learning with Data Heterogeneity
Xuefeng Han, Jun Li, Wen Chen +4
With the rapid proliferation of smart mobile devices, federated learning (FL) has been widely considered for application in wireless networks for distributed model training. Howeve…
Decentralized Federated Reinforcement Learning for User-Centric Dynamic TFDD Control
Ziyan Yin, Zhe Wang, Jun Li +3
The explosive growth of dynamic and heterogeneous data traffic brings great challenges for 5G and beyond mobile networks. To enhance the network capacity and reliability, we propos…