OpenMined/PySyft: papers linking to it

GitHub · 10,051 stars · 2,014 forks · Python · license Apache-2.0 · created 2017 · github.com/OpenMined/PySyft

← Code and data repositories cited in research papers

Research papers in the 480 tracked journals that link to OpenMined/PySyft (https://github.com/OpenMined/PySyft), read from their Crossref records: by year, category and journal, the family tree of papers building on one another, and the linked repositories derived from it or that it derives from.

Key findings#

4 papers in the tracked journals link to the GitHub repository OpenMined/PySyft in their Crossref records, the first in 2022; most of them (1) in 2025.
By category: AI (4); the journals with the most are Knowledge and Information Systems (2), Expert Systems with Applications (1) and Information Fusion (1).
2 of the papers build on one another: a later paper cites an earlier one that also links OpenMined/PySyft.

By year#

20222025

Papers linking to OpenMined/PySyft by publication year
Year	Papers
2022	1
2023	1
2024	1
2025	1

Journals#

Journals whose papers link to OpenMined/PySyft most
Journal	Papers
Knowledge and Information Systems	2
Expert Systems with Applications	1
Information Fusion	1

Family tree#

Each paper sits under the earlier paper it builds on; papers that cite no other paper here start a branch of their own.

From distributed machine learning to federated learning: a survey Knowledge and Information Systems, 2022
- Trustworthy federated learning: privacy, security, and beyond Knowledge and Information Systems, 2024 — cites the paper above

Papers linking to OpenMined/PySyft, most cited first
Published	Title	Journal	Authors	Citations	Link
2022-03-22	From distributed machine learning to federated learning: a survey	Knowledge and Information Systems	Ji Liu, Jizhou Huang, Yang Zhou et al.	360	cites it
2024-11-26	Trustworthy federated learning: privacy, security, and beyond	Knowledge and Information Systems	Chunlu Chen, Ji Liu, Haowen Tan et al.	87	cites it
2023-08-01	Federated learning based on Stackelberg game in unmanned-aerial-vehicle-enabled mobile edge computing	Expert Systems with Applications	Chunlin Li, Mingyang Song, Youlong Luo	50	cites it
2025-12-24	Parasite: planting durable backdoors against continual federated model fusion via discriminative feature pushing-pulling	Information Fusion	Tong Chen, Bowen Du, Jiejie Zhao et al.	1	cites it

How this is measured#

Links are read from each paper's Crossref record as its publisher deposited it: the URLs and repository DOIs in its reference list (the software and data it cites) and its supplementary links (“is-supplemented-by”: its own code or data). A paper that gives its code only in the text, a footnote or a data availability statement is not counted, and some publishers deposit reference lists without their URLs, so every count here is a lower bound. Repository details come from the GitHub REST API (stars, forks, and the repository a fork was made from), the Hugging Face Hub API (likes, downloads, and the base model a model was derived from) and DataCite (Zenodo and other data DOIs, and the GitHub release a Zenodo record archives), read again every month.

Cite this page

Scholar4AI. (2026). OpenMined/PySyft: papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/github-openmined-pysyft

@misc{scholar4ai-code-github-openmined-pysyft,
  author = {{Scholar4AI}},
  title = {OpenMined/PySyft: papers linking to it},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/code/github-openmined-pysyft}},
  urldate = {2026-11-03},
  note = {Journal and Conference Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}

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