5 papers
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…
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,…
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…
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…