39 citations · 78 across the 10 of their papers we have counts for
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Wirelessly Powered Federated Edge Learning: Optimal Tradeoffs Between Convergence and Power Transfer
Qunsong Zeng, Yuqing Du, Kaibin Huang
Federated edge learning (FEEL) is a widely adopted framework for training an artificial intelligence (AI) model distributively at edge devices to leverage their data while preservi…
Capacity of Remote Classification Over Wireless Channels
Qiao Lan, Yuqing Du, Petar Popovski +1
Wireless connectivity creates a computing paradigm that merges communication and inference. A basic operation in this paradigm is the one where a device offloads classification tas…
Energy-Efficient Resource Management for Federated Edge Learning with CPU-GPU Heterogeneous Computing
Qunsong Zeng, Yuqing Du, Kaibin Huang +1
Edge machine learning involves the deployment of learning algorithms at the network edge to leverage massive distributed data and computation resources to train artificial intellig…
One-Bit Over-the-Air Aggregation for Communication-Efficient Federated Edge Learning: Design and Convergence Analysis
Guangxu Zhu, Yuqing Du, Deniz Gunduz +1
Federated edge learning (FEEL) is a popular framework for model training at an edge server using data distributed at edge devices (e.g., smart-phones and sensors) without compromis…
An Introduction to Communication Efficient Edge Machine Learning
Qiao Lan, Zezhong Zhang, Yuqing Du +2
In the near future, Internet-of-Things (IoT) is expected to connect billions of devices (e.g., smartphones and sensors), which generate massive real-time data at the network edge.…
High-Dimensional Stochastic Gradient Quantization for Communication-Efficient Edge Learning
Yuqing Du, Sheng Yang, Kaibin Huang
Edge machine learning involves the deployment of learning algorithms at the wireless network edge so as to leverage massive mobile data for enabling intelligent applications. The m…