Chenghui Peng

675 total citations · 1 hit paper
37 papers, 318 citations indexed

About

Chenghui Peng is a scholar working on Artificial Intelligence, Computer Networks and Communications and Electrical and Electronic Engineering. According to data from OpenAlex, Chenghui Peng has authored 37 papers receiving a total of 318 indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Artificial Intelligence, 16 papers in Computer Networks and Communications and 16 papers in Electrical and Electronic Engineering. Recurrent topics in Chenghui Peng's work include Privacy-Preserving Technologies in Data (13 papers), Caching and Content Delivery (7 papers) and Stochastic Gradient Optimization Techniques (7 papers). Chenghui Peng is often cited by papers focused on Privacy-Preserving Technologies in Data (13 papers), Caching and Content Delivery (7 papers) and Stochastic Gradient Optimization Techniques (7 papers). Chenghui Peng collaborates with scholars based in China, Hong Kong and United Kingdom. Chenghui Peng's co-authors include Rongpeng Li, Zhifeng Zhao, Honggang Zhang, Pingyi Fan, Khaled B. Letaief, Jian Wu, Jiaxun Lu, Yunfeng Shao, Shuo Wan and Emmanouil Pateromichelakis and has published in prestigious journals such as IEEE Journal on Selected Areas in Communications, IEEE Communications Magazine and IEEE Transactions on Communications.

In The Last Decade

Chenghui Peng

30 papers receiving 309 citations

Hit Papers

NetGPT: An AI-Native Network Architecture for Provisionin... 2024 2026 2025 2024 10 20 30 40
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.

  1. NetGPT: An AI-Native Network Architecture for Provisioning Beyond Personalized Generative Services
    2024 40 citations Rongpeng Li, Zhifeng Zhao et al. profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Chenghui Peng China 8 153 129 102 23 19 37 318
Kwihoon Kim South Korea 7 131 0.9× 185 1.4× 126 1.2× 32 1.4× 22 1.2× 50 328
Shangqing Zhao United States 8 158 1.0× 79 0.6× 67 0.7× 25 1.1× 22 1.2× 26 247
Sabuzima Nayak India 10 80 0.5× 158 1.2× 72 0.7× 51 2.2× 30 1.6× 22 262
Bilal Saoud Algeria 9 63 0.4× 62 0.5× 58 0.6× 18 0.8× 14 0.7× 32 230
Sivaraman Eswaran India 8 73 0.5× 69 0.5× 40 0.4× 53 2.3× 20 1.1× 41 209
Yunlong Mao China 8 148 1.0× 67 0.5× 52 0.5× 44 1.9× 27 1.4× 28 241
Moirangthem Marjit Singh India 8 131 0.9× 210 1.6× 40 0.4× 21 0.9× 32 1.7× 26 306
Junling Yuan China 14 85 0.6× 133 1.0× 338 3.3× 48 2.1× 21 1.1× 52 485
Jiantao Yuan China 11 170 1.1× 185 1.4× 237 2.3× 26 1.1× 13 0.7× 51 412
Jingxiao Ma China 8 86 0.6× 119 0.9× 209 2.0× 23 1.0× 19 1.0× 27 287

Countries citing papers authored by Chenghui Peng

Since Specialization
Citations

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

Fields of papers citing papers by Chenghui Peng

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Chenghui Peng

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

All Works

20 of 20 papers shown
1.
2.
Li, Rongpeng, et al.. (2025). Snake Learning: A Communication- and Computation-Efficient Distributed Learning Framework for 6G. IEEE Communications Magazine. 63(5). 198–205. 1 indexed citations
3.
4.
Liu, Guangyi, Tong Zhou, Tianjiao Chen, et al.. (2024). 6G移动信息网络架构：从通信到一切皆服务的变迁. Scientia Sinica Informationis.
5.
Fan, Pingyi, et al.. (2024). Deep Conditional Generative Semantic Communication for Image Transmission. Rare & Special e-Zone (The Hong Kong University of Science and Technology). 1073–1078. 4 indexed citations
6.
Fan, Pingyi, et al.. (2024). SAM: An Efficient Approach With Selective Aggregation of Models in Federated Learning. IEEE Internet of Things Journal. 11(11). 20769–20783. 4 indexed citations
7.
Fan, Pingyi, et al.. (2024). ISFL: Federated Learning for Non-i.i.d. Data With Local Importance Sampling. IEEE Internet of Things Journal. 11(16). 27448–27462. 14 indexed citations
8.
Li, Rongpeng, Wang Fei, Chenghui Peng, et al.. (2023). Communication-Efficient Cooperative Multi-Agent PPO via Regulated Segment Mixture in Internet of Vehicles. 3003–3008. 1 indexed citations
9.
Peng, Chenghui, et al.. (2023). Towards Efficient Federated Learning: Layer-Wise Pruning-Quantization Scheme and Coding Design. Entropy. 25(8). 1205–1205. 5 indexed citations
10.
Fan, Pingyi, et al.. (2023). Enhanced Federated Learning on Non-Iid Data via Local Importance Sampling. Rare & Special e-Zone (The Hong Kong University of Science and Technology). 104–109. 1 indexed citations
11.
Fan, Pingyi, et al.. (2023). Learning Channel Capacity With Neural Mutual Information Estimator Based on Message Importance Measure. IEEE Transactions on Communications. 72(3). 1370–1384. 2 indexed citations
12.
Luo, Jun, et al.. (2023). FedLP: Layer-Wise Pruning Mechanism for Communication-Computation Efficient Federated Learning. Rare & Special e-Zone (The Hong Kong University of Science and Technology). 1250–1255. 18 indexed citations
13.
An, Xueli, et al.. (2023). Towards Native Support for Federated Learning in 6G. 1–6. 1 indexed citations
14.
Li, Rongpeng, et al.. (2022). HFedMTL: Hierarchical Federated Multi-Task Learning. 2022 IEEE 33rd Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC). 1–6. 4 indexed citations
15.
Wan, Shuo, Jiaxun Lu, Pingyi Fan, et al.. (2022). How Global Observation embedding in Vertical-Horizontal Federated Learning. 2022 International Wireless Communications and Mobile Computing (IWCMC). 12–17. 1 indexed citations
16.
Wan, Shuo, Jiaxun Lu, Pingyi Fan, et al.. (2021). Convergence Analysis and System Design for Federated Learning Over Wireless Networks. IEEE Journal on Selected Areas in Communications. 39(12). 3622–3639. 44 indexed citations
17.
Li, Rongpeng, et al.. (2021). Network AI Management & Orchestration: A Federated Multi-task Learning Case. 1–6. 1 indexed citations
18.
Pateromichelakis, Emmanouil, et al.. (2018). LAA as a Key Enabler in Slice-Aware 5G RAN: Challenges and Opportunities. IEEE Communications Standards Magazine. 2(1). 29–35. 10 indexed citations
19.
Tan, Wei, et al.. (2014). SDN-enabled converged networks. IEEE Wireless Communications. 21(6). 79–85. 12 indexed citations
20.
Li, Hong, Chenghui Peng, Bojie Li, et al.. (2010). User ID Routing Architecture. IEEE Vehicular Technology Magazine. 5(1). 62–69. 4 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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