41 citations · 229 across the 71 of their papers we have counts for
9 papers · 1 filter
Towards Full-scene Domain Generalization in Multi-agent Collaborative Bird's Eye View Segmentation for Connected and Autonomous Driving
Senkang Hu, Zhengru Fang, Yiqin Deng +3
Collaborative perception has recently gained significant attention in autonomous driving, improving perception quality by enabling the exchange of additional information among vehi…
FedSN: A Federated Learning Framework over Heterogeneous LEO Satellite Networks
Zheng Lin, Zhe Chen, Zihan Fang +3
Recently, a large number of Low Earth Orbit (LEO) satellites have been launched and deployed successfully in space by commercial companies, such as SpaceX. Due to multimodal sensor…
Adaptive Communications in Collaborative Perception with Domain Alignment for Autonomous Driving
Senkang Hu, Zhengru Fang, Haonan An +4
Collaborative perception among multiple connected and autonomous vehicles can greatly enhance perceptive capabilities by allowing vehicles to exchange supplementary information via…
Pushing Large Language Models to the 6G Edge: Vision, Challenges, and Opportunities
Zheng Lin, Guanqiao Qu, Qiyuan Chen +3
Large language models (LLMs), which have shown remarkable capabilities, are revolutionizing AI development and potentially shaping our future. However, given their multimodality, t…
Optimal Resource Allocation for U-Shaped Parallel Split Learning
Song Lyu, Zheng Lin, Guanqiao Qu +3
Split learning (SL) has emerged as a promising approach for model training without revealing the raw data samples from the data owners. However, traditional SL inevitably leaks lab…
Split Learning in 6G Edge Networks
Zheng Lin, Guanqiao Qu, Xianhao Chen +1
With the proliferation of distributed edge computing resources, the 6G mobile network will evolve into a network for connected intelligence. Along this line, the proposal to incorp…
Spectrum-aware Multi-hop Task Routing in Vehicle-assisted Collaborative Edge Computing
Yiqin Deng, Haixia Zhang, Xianhao Chen +1
Multi-access edge computing (MEC) is a promising technology to enhance the quality of service, particularly for low-latency services, by enabling computing offloading to edge serve…
Vehicle as a Service (VaaS): Leverage Vehicles to Build Service Networks and Capabilities for Smart Cities
Xianhao Chen, Yiqin Deng, Haichuan Ding +4
Smart cities demand resources for rich immersive sensing, ubiquitous communications, powerful computing, large storage, and high intelligence (SCCSI) to support various kinds of ap…
Efficient Parallel Split Learning over Resource-constrained Wireless Edge Networks
Zheng Lin, Guangyu Zhu, Yiqin Deng +4
The increasingly deeper neural networks hinder the democratization of privacy-enhancing distributed learning, such as federated learning (FL), to resource-constrained devices. To o…