Heqiang Wang

557 total citations · 1 hit paper
8 papers, 395 citations indexed

About

Heqiang Wang is a scholar working on Artificial Intelligence, Computer Networks and Communications and Electrical and Electronic Engineering. According to data from OpenAlex, Heqiang Wang has authored 8 papers receiving a total of 395 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Artificial Intelligence, 4 papers in Computer Networks and Communications and 3 papers in Electrical and Electronic Engineering. Recurrent topics in Heqiang Wang's work include Privacy-Preserving Technologies in Data (6 papers), Stochastic Gradient Optimization Techniques (2 papers) and Distributed Sensor Networks and Detection Algorithms (2 papers). Heqiang Wang is often cited by papers focused on Privacy-Preserving Technologies in Data (6 papers), Stochastic Gradient Optimization Techniques (2 papers) and Distributed Sensor Networks and Detection Algorithms (2 papers). Heqiang Wang collaborates with scholars based in China and United States. Heqiang Wang's co-authors include Jie Xu, Lixing Chen, Pingping Xiao, Zhang Yon, Weizhe Zhang, Xiaoxiong Zhong and Fangming Liu and has published in prestigious journals such as IEEE Transactions on Wireless Communications, IEEE Internet of Things Journal and IEEE Transactions on Mobile Computing.

In The Last Decade

Heqiang Wang

7 papers receiving 392 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.

Client Selection and Bandwidth Allocation in Wireless Federated Learning Networks: A Long-Term Perspective

2020 330 citations Jie Xu, Heqiang Wang IEEE Transactions on Wireless Communications profile →

Peers

Heqiang Wang

AI

CNC

EEE

CSA

IS

Wenqi Shi China

Mehdi Salehi Heydar Abad Türkiye

Zhida Jiang China

Saeed Vahidian United States

Tra Huong Thi Le South Korea

Anbu Huang Hong Kong

Bai Li Australia

Omar Hasan Jordan

Guoxing Zhan United States

Gavin Zheng Australia

Wenqi Shi China

Heqiang Wang

417 ×1.3

AI

264 ×1.5

CNC

241 ×1.6

EEE

68 ×1.0

CSA

34 ×1.0

IS

Citations per year, relative to Heqiang Wang Heqiang Wang (= 1×) peers Wenqi Shi

Countries citing papers authored by Heqiang Wang

Since Specialization

Citations

This map shows the geographic impact of Heqiang Wang'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 Heqiang Wang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Heqiang Wang more than expected).

Fields of papers citing papers by Heqiang Wang

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Heqiang Wang. 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 Heqiang Wang. The network helps show where Heqiang Wang may publish in the future.

Co-authorship network of co-authors of Heqiang Wang

This figure shows the co-authorship network connecting the top 25 collaborators of Heqiang Wang. A scholar is included among the top collaborators of Heqiang Wang 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 Heqiang Wang. Heqiang Wang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

8 of 8 papers shown

1.

Wang, Heqiang, et al.. (2025). Computation and Communication Efficient Lightweighting Vertical Federated Learning for Smart Building IoT. 1–6. 1 indexed citations

2.

Wang, Heqiang, et al.. (2025). Denoising and Adaptive Online Vertical Federated Learning for Sequential Multi-Sensor Data in IIoT. IEEE Transactions on Mobile Computing. 25(2). 2350–2364.

3.

Wang, Heqiang & Jie Xu. (2024). Online Vertical Federated Learning for Cooperative Spectrum Sensing. IEEE Transactions on Cognitive Communications and Networking. 11(4). 2317–2326. 2 indexed citations

4.

Wang, Heqiang & Jie Xu. (2024). Friends to Help: Saving Federated Learning from Client Dropout. 8896–8900. 7 indexed citations

5.

Wang, Heqiang, et al.. (2023). On the Local Cache Update Rules in Streaming Federated Learning. IEEE Internet of Things Journal. 11(6). 10808–10816. 5 indexed citations

6.

Xu, Jie, Heqiang Wang, & Lixing Chen. (2021). Bandwidth Allocation for Multiple Federated Learning Services in Wireless Edge Networks. IEEE Transactions on Wireless Communications. 21(4). 2534–2546. 48 indexed citations

7.

Xu, Jie & Heqiang Wang. (2020). Client Selection and Bandwidth Allocation in Wireless Federated Learning Networks: A Long-Term Perspective. IEEE Transactions on Wireless Communications. 20(2). 1188–1200. 330 indexed citations breakdown →

8.

Yon, Zhang, et al.. (2013). Research on Teaching Ability Evaluation Model of University Teachers Based on Fuzzy Theory. 2. 1879–1881. 2 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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