Wenqi Shi

792 total citations · 1 hit paper
8 papers, 546 citations indexed

About

Wenqi Shi is a scholar working on Computer Networks and Communications, Artificial Intelligence and Computer Vision and Pattern Recognition. According to data from OpenAlex, Wenqi Shi has authored 8 papers receiving a total of 546 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Computer Networks and Communications, 6 papers in Artificial Intelligence and 3 papers in Computer Vision and Pattern Recognition. Recurrent topics in Wenqi Shi's work include Age of Information Optimization (5 papers), Privacy-Preserving Technologies in Data (5 papers) and IoT and Edge/Fog Computing (3 papers). Wenqi Shi is often cited by papers focused on Age of Information Optimization (5 papers), Privacy-Preserving Technologies in Data (5 papers) and IoT and Edge/Fog Computing (3 papers). Wenqi Shi collaborates with scholars based in China and Japan. Wenqi Shi's co-authors include Sheng Zhou, Zhisheng Niu, Lu Geng, Miao Jiang, Yang Zhang, Yunzhong Hou, Yuxuan Sun, Lu Geng, Mei Yang and Teng Su and has published in prestigious journals such as IEEE Communications Magazine, IEEE Transactions on Wireless Communications and IEEE Transactions on Parallel and Distributed Systems.

In The Last Decade

Wenqi Shi

8 papers receiving 541 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Joint Device Scheduling and Resource Allocation for Latency Constrained Wireless Federated Learning

2020 270 citations Wenqi Shi, Sheng Zhou et al. IEEE Transactions on Wireless Communications profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Wenqi Shi China	6	417	264	241	68	46	8	546
Víctor Valls Ireland	8	237 0.6×	189 0.7×	157 0.7×	33 0.5×	49 1.1×	26	443
Zhiyuan Xie Hong Kong	6	197 0.5×	64 0.2×	57 0.2×	32 0.5×	101 2.2×	8	300
Jian-Jhih Kuo Taiwan	11	81 0.2×	247 0.9×	114 0.5×	20 0.3×	36 0.8×	56	356
Ziling Wei China	10	109 0.3×	239 0.9×	141 0.6×	8 0.1×	67 1.5×	42	370
Yunlong Mao China	8	148 0.4×	67 0.3×	52 0.2×	22 0.3×	27 0.6×	28	241
Mattia Merluzzi Italy	11	91 0.2×	243 0.9×	217 0.9×	9 0.1×	29 0.6×	35	395
Ashutosh Bhatia India	12	109 0.3×	211 0.8×	86 0.4×	10 0.1×	37 0.8×	56	352
Triet D. Vo-Huu United States	8	160 0.4×	74 0.3×	126 0.5×	15 0.2×	46 1.0×	11	275
Marco V. Barbera Italy	6	69 0.2×	414 1.6×	230 1.0×	39 0.6×	57 1.2×	7	484

Countries citing papers authored by Wenqi Shi

Since Specialization

Citations

This map shows the geographic impact of Wenqi Shi's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Wenqi Shi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Wenqi Shi more than expected).

Fields of papers citing papers by Wenqi Shi

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Wenqi Shi. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Wenqi Shi. The network helps show where Wenqi Shi may publish in the future.

Co-authorship network of co-authors of Wenqi Shi

This figure shows the co-authorship network connecting the top 25 collaborators of Wenqi Shi. A scholar is included among the top collaborators of Wenqi Shi based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Wenqi Shi. Wenqi Shi is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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8 of 8 papers shown

1.

Yang, Mei, Lifu Zhang, Teng Su, et al.. (2025). EfficientMoE: Optimizing Mixture-of-Experts Model Training With Adaptive Load Balance. IEEE Transactions on Parallel and Distributed Systems. 36(4). 677–688. 1 indexed citations

2.

Shi, Wenqi, Sheng Zhou, Zhisheng Niu, Miao Jiang, & Lu Geng. (2022). Multiuser Co-Inference With Batch Processing Capable Edge Server. IEEE Transactions on Wireless Communications. 22(1). 286–300. 24 indexed citations

3.

Shi, Wenqi, Yuxuan Sun, Sheng Zhou, & Zhisheng Niu. (2021). Device Scheduling and Resource Allocation for Federated Learning under Delay and Energy Constraints. 596–600. 3 indexed citations

4.

Sun, Yuxuan, et al.. (2020). Edge Learning with Timeliness Constraints: Challenges and Solutions. IEEE Communications Magazine. 58(12). 27–33. 22 indexed citations

5.

Shi, Wenqi, Sheng Zhou, & Zhisheng Niu. (2020). Device Scheduling with Fast Convergence for Wireless Federated Learning. 1–6. 160 indexed citations

6.

Shi, Wenqi, Sheng Zhou, Zhisheng Niu, Miao Jiang, & Lu Geng. (2020). Joint Device Scheduling and Resource Allocation for Latency Constrained Wireless Federated Learning. IEEE Transactions on Wireless Communications. 20(1). 453–467. 270 indexed citations breakdown →

7.

Shi, Wenqi, Yunzhong Hou, Sheng Zhou, et al.. (2019). Improving Device-Edge Cooperative Inference of Deep Learning via 2-Step Pruning. arXiv (Cornell University). 1–6. 60 indexed citations

8.

Shi, Wenqi, Yunzhong Hou, Sheng Zhou, et al.. (2019). Improving Device-Edge Cooperative Inference of Deep Learning via 2-Step Pruning. arXiv (Cornell University). 1–6. 6 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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