Zelei Liu

781 total citations
11 papers, 407 citations indexed

About

Zelei Liu is a scholar working on Artificial Intelligence, Information Systems and Computer Science Applications. According to data from OpenAlex, Zelei Liu has authored 11 papers receiving a total of 407 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Artificial Intelligence, 5 papers in Information Systems and 4 papers in Computer Science Applications. Recurrent topics in Zelei Liu's work include Privacy-Preserving Technologies in Data (9 papers), Mobile Crowdsensing and Crowdsourcing (4 papers) and Blockchain Technology Applications and Security (3 papers). Zelei Liu is often cited by papers focused on Privacy-Preserving Technologies in Data (9 papers), Mobile Crowdsensing and Crowdsourcing (4 papers) and Blockchain Technology Applications and Security (3 papers). Zelei Liu collaborates with scholars based in China, Singapore and Hong Kong. Zelei Liu's co-authors include Han Yu, Qiang Yang, Tianjian Chen, Mingshu Cong, Dusit Niyato, Xi Weng, Yuanyuan Chen, Yang Liu, Yang Liu and Lizhen Cui and has published in prestigious journals such as SHILAP Revista de lepidopterología, Future Generation Computer Systems and IEEE Intelligent Systems.

In The Last Decade

Zelei Liu

11 papers receiving 400 citations

Peers

Zelei Liu

AI

CSA

IS

SPS

CNC

Anbu Huang Hong Kong

Honglu Jiang China

Ahmed Moustafa Japan

Alberto Blanco-Justicia Spain

Kuan Lun Huang Australia

Sheng Zhong China

Gavin Zheng Australia

Phil Greenwood United Kingdom

Layla Pournajaf United States

Yuanqin He China

Anbu Huang Hong Kong

Zelei Liu

275 ×0.7

AI

43 ×0.4

CSA

44 ×0.5

IS

28 ×0.4

SPS

42 ×0.9

CNC

Citations per year, relative to Zelei Liu Zelei Liu (= 1×) peers Anbu Huang

Countries citing papers authored by Zelei Liu

Since Specialization

Citations

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

Fields of papers citing papers by Zelei Liu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Zelei Liu

This figure shows the co-authorship network connecting the top 25 collaborators of Zelei Liu. A scholar is included among the top collaborators of Zelei Liu 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 Zelei Liu. Zelei Liu 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:

11 of 11 papers shown

1.

Li, Zengxiang, et al.. (2024). Federated Learning in Large Model Era: Vision-Language Model for Smart City Safety Operation Management. 1578–1585. 7 indexed citations

2.

Li, Quanhong, et al.. (2024). pFedCE: Personalized Federated Learning Based on Contribution Evaluation. 262–270. 1 indexed citations

3.

Hu, Jiejun, et al.. (2024). Federated data acquisition market: Architecture and a mean-field based data pricing strategy. SHILAP Revista de lepidopterología. 5(1). 100232–100232. 4 indexed citations

4.

Chen, Yuanyuan, Zichen Chen, Sheng Guo, et al.. (2023). Efficient Training of Large-Scale Industrial Fault Diagnostic Models through Federated Opportunistic Block Dropout. Proceedings of the AAAI Conference on Artificial Intelligence. 37(13). 15485–15493. 6 indexed citations

5.

Liu, Zelei, Yuanyuan Chen, Han Yu, et al.. (2023). CAreFL: Enhancing smart healthcare with Contribution‐Aware Federated Learning. AI Magazine. 44(1). 4–15. 2 indexed citations

6.

Liu, Zelei, Yuanyuan Chen, Han Yu, Yang Liu, & Lizhen Cui. (2022). GTG-Shapley: Efficient and Accurate Participant Contribution Evaluation in Federated Learning. ACM Transactions on Intelligent Systems and Technology. 13(4). 1–21. 85 indexed citations

7.

Liu, Zelei, Yuanyuan Chen, Han Yu, et al.. (2022). Contribution-Aware Federated Learning for Smart Healthcare. Proceedings of the AAAI Conference on Artificial Intelligence. 36(11). 12396–12404. 47 indexed citations

8.

Yu, Han, Zelei Liu, Yang Liu, et al.. (2020). A Fairness-aware Incentive Scheme for Federated Learning. Proceedings of the AAAI/ACM Conference on AI Ethics and Society. 393–399. 142 indexed citations

9.

Chen, Zichen, et al.. (2020). A Multi-player Game for Studying Federated Learning Incentive Schemes. Rare & Special e-Zone (The Hong Kong University of Science and Technology). 5279–5281. 26 indexed citations

10.

Yu, Han, Zelei Liu, Yang Liu, et al.. (2020). A Sustainable Incentive Scheme for Federated Learning. IEEE Intelligent Systems. 35(4). 58–69. 83 indexed citations

11.

Liu, Zelei, et al.. (2017). A novel process-based association rule approach through maximal frequent itemsets for big data processing. Future Generation Computer Systems. 81. 414–424. 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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