127 citations · 559 across the 76 of their papers we have counts for
9 papers · 1 filter
Graph Neural Networks Meet Wireless Communications: Motivation, Applications, and Future Directions
Mengyuan Lee, Guanding Yu, Huaiyu Dai +1
As an efficient graph analytical tool, graph neural networks (GNNs) have special properties that are particularly fit for the characteristics and requirements of wireless communica…
Multi-Job Intelligent Scheduling with Cross-Device Federated Learning
Ji Liu, Juncheng Jia, Beichen Ma +5
Recent years have witnessed a large amount of decentralized data in various (edge) devices of end-users, while the decentralized data aggregation remains complicated for machine le…
Resource Constrained Vehicular Edge Federated Learning with Highly Mobile Connected Vehicles
Md Ferdous Pervej, Richeng Jin, Huaiyu Dai
This paper proposes a vehicular edge federated learning (VEFL) solution, where an edge server leverages highly mobile connected vehicles' (CVs') onboard central processing units (C…
RFID: Towards Low Latency and Reliable DAG Task Scheduling over Dynamic Vehicular Clouds
Zhang Liu, Minghui Liwang, Seyyedali Hosseinalipour +3
Vehicular cloud (VC) platforms integrate heterogeneous and distributed resources of moving vehicles to offer timely and cost-effective computing services. However, the dynamic natu…
Privacy-Preserving Decentralized Inference with Graph Neural Networks in Wireless Networks
Mengyuan Lee, Guanding Yu, Huaiyu Dai
As an efficient neural network model for graph data, graph neural networks (GNNs) recently find successful applications for various wireless optimization problems. Given that the i…
Gradient Obfuscation Gives a False Sense of Security in Federated Learning
Kai Yue, Richeng Jin, Chau-Wai Wong +2
Federated learning has been proposed as a privacy-preserving machine learning framework that enables multiple clients to collaborate without sharing raw data. However, client priva…
Mobility, Communication and Computation Aware Federated Learning for Internet of Vehicles
Md Ferdous Pervej, Jianlin Guo, Kyeong Jin Kim +7
While privacy concerns entice connected and automated vehicles to incorporate on-board federated learning (FL) solutions, an integrated vehicle-to-everything communication with het…
FedDUAP: Federated Learning with Dynamic Update and Adaptive Pruning Using Shared Data on the Server
Hong Zhang, Ji Liu, Juncheng Jia +3
Despite achieving remarkable performance, Federated Learning (FL) suffers from two critical challenges, i.e., limited computational resources and low training efficiency. In this p…
Efficient Content Delivery in User-Centric and Cache-Enabled Vehicular Edge Networks with Deadline-Constrained Heterogeneous Demands
Md Ferdous Pervej, Richeng Jin, Shih-Chun Lin +1
Modern connected vehicles (CVs) frequently require diverse types of content for mission-critical decision-making and onboard users' entertainment. These contents are required to be…