Zhida Jiang

535 total citations
13 papers, 329 citations indexed

About

Zhida Jiang is a scholar working on Artificial Intelligence, Computer Networks and Communications and Computer Science Applications. According to data from OpenAlex, Zhida Jiang has authored 13 papers receiving a total of 329 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Artificial Intelligence, 5 papers in Computer Networks and Communications and 4 papers in Computer Science Applications. Recurrent topics in Zhida Jiang's work include Privacy-Preserving Technologies in Data (12 papers), Stochastic Gradient Optimization Techniques (8 papers) and Mobile Crowdsensing and Crowdsourcing (4 papers). Zhida Jiang is often cited by papers focused on Privacy-Preserving Technologies in Data (12 papers), Stochastic Gradient Optimization Techniques (8 papers) and Mobile Crowdsensing and Crowdsourcing (4 papers). Zhida Jiang collaborates with scholars based in China and United States. Zhida Jiang's co-authors include Hongli Xu, Yang Xu, Liusheng Huang, He Huang, Zhiyuan Wang, Chunming Qiao, Jianchun Liu, Yangming Zhao, Xu Yang and Wuyang Zhang and has published in prestigious journals such as IEEE Journal on Selected Areas in Communications, IEEE/ACM Transactions on Networking and IEEE Transactions on Parallel and Distributed Systems.

In The Last Decade

Zhida Jiang

12 papers receiving 325 citations

Peers

Zhida Jiang

CNC

EEE

CSA

Heqiang Wang China

Ilias Tsingenopoulos Belgium

Yuanqin He China

Zhiyi Tian Australia

Gavin Zheng Australia

Bai Li Australia

Xiaoming Huang China

Jiale Guo China

Vera Rimmer Belgium

Heqiang Wang China

Zhida Jiang

331 ×1.1

174 ×1.7

CNC

33 ×0.6

154 ×3.1

EEE

69 ×2.3

CSA

Citations per year, relative to Zhida Jiang Zhida Jiang (= 1×) peers Heqiang Wang

Countries citing papers authored by Zhida Jiang

Since Specialization

Citations

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

Fields of papers citing papers by Zhida Jiang

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Zhida Jiang

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

All Works

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13 of 13 papers shown

Jiang, Zhida, Yang Xu, Hongli Xu, et al.. (2024). Semi-Supervised Decentralized Machine Learning With Device-to-Device Cooperation. IEEE Transactions on Mobile Computing. 23(10). 9757–9771. 3 indexed citations

Jiang, Zhida, Xu Yang, Hongli Xu, Zhiyuan Wang, & Chunming Qiao. (2024). Clients Help Clients: Alternating Collaboration for Semi-Supervised Federated Learning. 1847–1860.

Xu, Yang, Yunming Liao, Lun Wang, et al.. (2024). Overcoming Noisy Labels and Non-IID Data in Edge Federated Learning. IEEE Transactions on Mobile Computing. 23(12). 11406–11421. 9 indexed citations

Wang, Zhiyuan, et al.. (2023). FAST: Enhancing Federated Learning Through Adaptive Data Sampling and Local Training. IEEE Transactions on Parallel and Distributed Systems. 35(2). 221–236. 6 indexed citations

Jiang, Zhida, Yang Xu, Hongli Xu, et al.. (2023). Joint Model Pruning and Topology Construction for Accelerating Decentralized Machine Learning. IEEE Transactions on Parallel and Distributed Systems. 34(10). 2827–2842. 5 indexed citations

Jiang, Zhida, Yang Xu, Hongli Xu, Zhiyuan Wang, & Qian Chen. (2023). Heterogeneity-Aware Federated Learning with Adaptive Client Selection and Gradient Compression. 1–10. 17 indexed citations

Xu, Yang, Zhida Jiang, Hongli Xu, et al.. (2023). Federated Learning With Client Selection and Gradient Compression in Heterogeneous Edge Systems. IEEE Transactions on Mobile Computing. 23(5). 5446–5461. 12 indexed citations

Jiang, Zhida, Yang Xu, Hongli Xu, et al.. (2023). Computation and Communication Efficient Federated Learning With Adaptive Model Pruning. IEEE Transactions on Mobile Computing. 23(3). 2003–2021. 43 indexed citations

Wang, Lun, Yang Xu, Hongli Xu, et al.. (2023). BOSE: Block-Wise Federated Learning in Heterogeneous Edge Computing. IEEE/ACM Transactions on Networking. 32(2). 1362–1377. 8 indexed citations

10.

Xu, Yang, et al.. (2022). Decentralized Federated Learning With Intermediate Results in Mobile Edge Computing. IEEE Transactions on Mobile Computing. 23(1). 341–358. 14 indexed citations

11.

Jiang, Zhida, Yang Xu, Hongli Xu, et al.. (2022). FedMP: Federated Learning through Adaptive Model Pruning in Heterogeneous Edge Computing. 2022 IEEE 38th International Conference on Data Engineering (ICDE). 767–779. 53 indexed citations

12.

Wang, Zhiyuan, Hongli Xu, Xu Yang, Zhida Jiang, & Jianchun Liu. (2022). CoopFL: Accelerating federated learning with DNN partitioning and offloading in heterogeneous edge computing. Computer Networks. 220. 109490–109490. 19 indexed citations

13.

Xu, Yang, et al.. (2021). FedSA: A Semi-Asynchronous Federated Learning Mechanism in Heterogeneous Edge Computing. IEEE Journal on Selected Areas in Communications. 39(12). 3654–3672. 140 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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