20 citations · 59 across the 26 of their papers we have counts for
10 papers · 1 filter
Analysis and Optimization of Wireless Multimodal Federated Learning on Modal Heterogeneity
Xuefeng Han, Wen Chen, Jun Li +6
Multimodal federated learning (MFL) is a distributed framework for training multimodal models without uploading local multimodal data of clients, thereby effectively protecting cli…
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…
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…
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…
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…
Mobility-Aware Joint User Scheduling and Resource Allocation for Low Latency Federated Learning
Kecheng Fan, Wen Chen, Jun Li +3
As an efficient distributed machine learning approach, Federated learning (FL) can obtain a shared model by iterative local model training at the user side and global model aggrega…
Gradient Sparsification for Efficient Wireless Federated Learning with Differential Privacy
Kang Wei, Jun Li, Chuan Ma +5
Federated learning (FL) enables distributed clients to collaboratively train a machine learning model without sharing raw data with each other. However, it suffers the leakage of p…
Low-latency Federated Learning with DNN Partition in Distributed Industrial IoT Networks
Xiumei Deng, Jun Li, Chuan Ma +4
Federated Learning (FL) empowers Industrial Internet of Things (IIoT) with distributed intelligence of industrial automation thanks to its capability of distributed machine learnin…
Blockchain Assisted Federated Learning over Wireless Channels: Dynamic Resource Allocation and Client Scheduling
Xiumei Deng, Jun Li, Chuan Ma +5
The blockchain technology has been extensively studied to enable distributed and tamper-proof data processing in federated learning (FL). Most existing blockchain assisted FL (BFL)…