Lingyang Chu

2.2k citations
46 papers · 1.4k indexed · 2 hit papers · h-index 15

Impact in

Papers in

Lingyang Chu

45 papers receiving 1.4k citations

Hit Papers

Personalized Cross-Silo Federated Learning on Non-IID Data 2021 · 426 citations
4262021202620222024100200300400

Peers

Lingyang Chu
Comparison fields: 5 of 136
  • Artificial Intelligence 723
  • Computer Science Applications 71
  • Water Science and Technology 162
  • Environmental Engineering 164
  • Health Informatics 14
Replace Tao Shen with:
Tao Shen China
Jixue Liu Australia
Xusheng Xiao United States
Raquel Martínez‐España Spain
Florin Leon Romania
Lili Jiang Sweden
Yunliang Chen China
Xiaohui Liang China
Lingyang Chu relative to Tao Shen China Tao Shen's profile →
Citations per field
00.5×10×15×20.5×
Tao Shen · 1×
Citations per year

Countries citing papers authored by Lingyang Chu

Since Specialization
Citations

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

Fields of papers citing papers by Lingyang Chu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Lingyang Chu, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Lingyang Chu Line = papers co-authored together Lingyang Chu links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 46 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Personalized Cross-Silo Federated Learning on Non-IID Data
Hit paper breakdown →
2021426
2
Model complexity of deep learning: a survey
Hit paper breakdown →
2021255
3 2011126
4 2011100
5 201661
6 201153
7 201848
8 201741
9 201334
10 201432
11 201628
12 201326
13 201924
14 202124
15 202215
16 201511
17
Personalized Federated Learning: An Attentive Collaboration Approach.
202010
18 201810
19 201810
20 202110

About Lingyang Chu

Lingyang Chu is a scholar working on Artificial Intelligence, Statistical and Nonlinear Physics, Signal Processing, Computer Science Applications and Computer Vision and Pattern Recognition, having authored 46 papers that have together received 1.4k indexed citations. Recurring topics across this work include Privacy-Preserving Technologies in Data (8 papers), Complex Network Analysis Techniques (7 papers), Advanced Graph Neural Networks (7 papers), Data Mining Algorithms and Applications (7 papers), Data Management and Algorithms (6 papers), Adversarial Robustness in Machine Learning (6 papers), Machine Learning and Data Classification (6 papers) and Advanced Image and Video Retrieval Techniques (6 papers). The work is most often cited by research in Artificial Intelligence (723 citations), Computer Science Applications (71 citations), Water Science and Technology (162 citations), Environmental Engineering (164 citations) and Health Informatics (14 citations). Lingyang Chu has collaborated with scholars based in Canada, China and United States. Frequent co-authors include Jian Pei, Lanjun Wang, Zirui Zhou, Yong Zhang, Jiangchuan Liu, Yutao Huang, Dengjun Wang, Dongmei Zhou, Weiqing Liu and Jiang Bian. Their work appears in journals such as Proceedings of the VLDB Endowment, IEEE Transactions on Knowledge and Data Engineering, Journal of Colloid and Interface Science, The Science of The Total Environment and Data Mining and Knowledge Discovery.

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.

Explore authors with similar magnitude of impact

Rankless by CCL
2026