Search Results for author: Dingzhu Wen

Found 7 papers, 0 papers with code

Task-Oriented Over-the-Air Computation for Multi-Device Edge AI

no code implementations2 Nov 2022 Dingzhu Wen, Xiang Jiao, Peixi Liu, Guangxu Zhu, Yuanming Shi, Kaibin Huang

To design inference-oriented AirComp, the transmit precoders at edge devices and receive beamforming at edge server are jointly optimized to rein in the aggregation error and maximize the inference accuracy.

Decision Making

Task-Oriented Sensing, Computation, and Communication Integration for Multi-Device Edge AI

no code implementations3 Jul 2022 Dingzhu Wen, Peixi Liu, Guangxu Zhu, Yuanming Shi, Jie Xu, Yonina C. Eldar, Shuguang Cui

This paper studies a new multi-device edge artificial-intelligent (AI) system, which jointly exploits the AI model split inference and integrated sensing and communication (ISAC) to enable low-latency intelligent services at the network edge.

Management Quantization

Federated Dropout -- A Simple Approach for Enabling Federated Learning on Resource Constrained Devices

no code implementations30 Sep 2021 Dingzhu Wen, Ki-Jun Jeon, Kaibin Huang

To tackle the challenge, in this paper, a federated dropout (FedDrop) scheme is proposed building on the classic dropout scheme for random model pruning.

Federated Learning

Adaptive Subcarrier, Parameter, and Power Allocation for Partitioned Edge Learning Over Broadband Channels

no code implementations8 Oct 2020 Dingzhu Wen, Ki-Jun Jeon, Mehdi Bennis, Kaibin Huang

Targeting broadband channels, we consider the joint control of parameter allocation, sub-channel allocation, and transmission power to improve the performance of PARTEL.

Scheduling for Cellular Federated Edge Learning with Importance and Channel Awareness

no code implementations1 Apr 2020 Jinke Ren, Yinghui He, Dingzhu Wen, Guanding Yu, Kaibin Huang, Dongning Guo

In this paper, a novel scheduling policy is proposed to exploit both diversity in multiuser channels and diversity in the "importance" of the edge devices' learning updates.


Joint Parameter-and-Bandwidth Allocation for Improving the Efficiency of Partitioned Edge Learning

no code implementations10 Mar 2020 Dingzhu Wen, Mehdi Bennis, Kaibin Huang

To this end, in each iteration, the model is dynamically partitioned into parametric blocks, which are downloaded to worker groups for updating using data subsets.

An Overview of Data-Importance Aware Radio Resource Management for Edge Machine Learning

no code implementations10 Nov 2019 Dingzhu Wen, Xiaoyang Li, Qunsong Zeng, Jinke Ren, Kaibin Huang

Specifically, the metrics that measure data importance in active learning (e. g., classification uncertainty and data diversity) are applied to RRM for efficient acquisition of distributed data in wireless networks to train AI models at servers.

Active Learning BIG-bench Machine Learning +1

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