most citedCommunication-Efficient Robust Federated Learning Over Heterogeneous Datasets

13 citations · 35 across the 8 of their papers we have counts for

collaborators

10 papers

cs.IT20212 cited

Age of Information in Physical-Layer Network Coding Enabled Two-Way Relay Networks

Haoyuan Pan, Tse-Tin Chan, Victor C. M. Leung +1

This paper investigates the information freshness of two-way relay networks (TWRN) operated with physical-layer network coding (PNC). Information freshness is quantified by age of…

cs.NI20217 cited

Customized Slicing for 6G: Enforcing Artificial Intelligence on Resource Management

Wanqing Guan, Haijun Zhang, Victor C. M. Leung

Next generation wireless networks are expected to support diverse vertical industries and offer countless emerging use cases. To satisfy stringent requirements of diversified servi…

cs.IT20204 cited

Caching Transient Content for IoT Sensing: Multi-Agent Soft Actor-Critic

Xiongwei Wu, Xiuhua Li, Jun Li +3

Edge nodes (ENs) in Internet of Things commonly serve as gateways to cache sensing data while providing accessing services for data consumers. This paper considers multiple ENs tha…

cs.NI2020

Edge Network-Assisted Real-Time Object Detection Framework for Autonomous Driving

Seung Wook Kim, Keunsoo Ko, Haneul Ko +1

Autonomous vehicles (AVs) can achieve the desired results within a short duration by offloading tasks even requiring high computational power (e.g., object detection (OD)) to edge…

cs.IT2020

Device-Clustering and Rate-Splitting Enabled Device-to-Device Cooperation Framework in Fog Radio Access Network

Md. Zoheb Hassan, Md. Jahangir Hossain, Julian Cheng +1

Resource allocation is investigated to enhance the performance of device-to-device (D2D) cooperation in a fog radio access network (F-RAN) architecture. Our envisioned framework en…

cs.LG202013 cited

Communication-Efficient Robust Federated Learning Over Heterogeneous Datasets

Yanjie Dong, Georgios B. Giannakis, Tianyi Chen +3

This work investigates fault-resilient federated learning when the data samples are non-uniformly distributed across workers, and the number of faulty workers is unknown to the cen…