Xinwei Zhang: h-index, Total Citations, and Citation Map
Xinwei Zhang's h-index is 11 (13 i10-index, 1,372+ total citations across 25+ publications) according to Google Scholar as of May 2026. Xinwei Zhang is affiliated with University of Southern California.
Xinwei Zhang is a researcher affiliated with University of Southern California, specializing in federated learning, distributed optimization, differential privacy. Their work has been cited 1,372 times. This profile visualizes their global influence, highlighting strong citation networks in United States.
Xinwei Zhang's Citation Metrics
Bibliometric impact based on 25 indexed publications.
- H-Index
- 11
- i10-Index
- 13
- Total Citations
- 1,372
- Citing Countries
- 26
As of May 2026.
Xinwei Zhang has an h-index of 11 and 1,372 total citations across 25 publications, with research cited by institutions in 26 countries.
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Top Cited Works
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FedPD: A federated learning framework with adaptivity to non-IID data
2021412
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Top Citing Institutions
Visa Evidence Package
Views and exports tuned for EB-1A, O-1A, and EB-2 NIW petitions. Sustained acclaim, geographic reach, and independent-citation filtering are the strongest evidence categories immigration adjudicators look for.
Significant Contributions
Auto-detected research lines — a seminal paper and the follow-up work building on it. Review and edit before using in a petition. Each Free PDF opens in a new tab — EB-1A organises this into the structure USCIS applies to Criterion 5 of 8 CFR § 204.5(h)(3)(v); EB-1B re-frames it under § 204.5(i)(3) (outstanding researcher); NIW presents it under prong 2 of Matter of Dhanasar.
781 citing papers could not be classified (no author data) — excluded from the percentages above.
The researcher developed a federated learning framework adaptive to non-IID data, establishing a foundational approach that subsequent work extended to privacy and convergence analysis.
The researcher developed FedBCD, a communication-efficient collaborative learning framework for distributed features, establishing a foundational approach for optimizing federated systems with vertically partitioned data.
The researcher developed a gradient-tracking framework for decentralized nonconvex optimization, establishing a theoretical foundation that subsequent work extended to federated learning and streaming data contexts.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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