---
title: "FederatedAI/FATE: papers citing this GitHub repository"
description: "7 papers in the tracked journals link to the GitHub repository FederatedAI/FATE in their Crossref records, the first in 2020; most of them (2) in 2024."
url: https://scholar4ai.com/code/github-federatedai-fate
markdown_url: https://scholar4ai.com/code/github-federatedai-fate.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "7 papers in the tracked journals link to the GitHub repository FederatedAI/FATE in their Crossref records, the first in 2020; most of them (2) in 2024. (as of 2026-11-03)"
data_url: https://scholar4ai.com/data/rankings.json
updated: 2026-11-03T14:43:26+09:00
window: 2026-10-05 to 2026-11-03
source: OpenAlex (CC0)
---

# FederatedAI/FATE: papers linking to it

Research papers in the 480 tracked journals that link to FederatedAI/FATE (https://github.com/FederatedAI/FATE), 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.

- GitHub
- 6,111 stars
- 1,568 forks
- Python
- license Apache-2.0
- created 2019
- https://github.com/FederatedAI/FATE

All repositories: https://scholar4ai.com/code.md

## Key findings

- 7 papers in the tracked journals link to the GitHub repository FederatedAI/FATE in their Crossref records, the first in 2020; most of them (2) in 2024.
- By category: AI (6) and Operations Research (1); the journals with the most are Knowledge and Information Systems (3), Information Fusion (2) and Computers & Industrial Engineering (1).
- 2 of the papers build on one another: a later paper cites an earlier one that also links FederatedAI/FATE.

## By year

*Papers linking to FederatedAI/FATE by publication year*

| Year | Papers |
|---|---:|
| 2020 | 1 |
| 2022 | 1 |
| 2023 | 1 |
| 2024 | 2 |
| 2025 | 1 |
| 2026 | 1 |

## Journals

*Journals whose papers link to FederatedAI/FATE most*

| Journal | Papers |
|---|---:|
| [Knowledge and Information Systems](https://scholar4ai.com/journals/knowledge-and-information-systems.md) | 3 |
| [Information Fusion](https://scholar4ai.com/journals/information-fusion.md) | 2 |
| [Computers & Industrial Engineering](https://scholar4ai.com/journals/computers-industrial-engineering.md) | 1 |
| [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | 1 |

## Family tree

Each paper sits under the earlier paper it builds on.

- [From distributed machine learning to federated learning: a survey](https://doi.org/10.1007/s10115-022-01664-x) (Knowledge and Information Systems, 2022)
  - [Trustworthy federated learning: privacy, security, and beyond](https://doi.org/10.1007/s10115-024-02285-2) (Knowledge and Information Systems, 2024) — cites the paper above

## Papers

*Papers linking to FederatedAI/FATE, most cited first*

| Published | Title | Journal | Authors | Citations | Link |
|---|---|---|---|---:|---|
| 2020-09-17 | [A review of applications in federated learning](https://doi.org/10.1016/j.cie.2020.106854) | [Computers & Industrial Engineering](https://scholar4ai.com/journals/computers-industrial-engineering.md) | Li Li, Yuxi Fan, Ying Kei Tse et al. | 1,524 | cites it |
| 2022-03-22 | [From distributed machine learning to federated learning: a survey](https://doi.org/10.1007/s10115-022-01664-x) | [Knowledge and Information Systems](https://scholar4ai.com/journals/knowledge-and-information-systems.md) | Ji Liu, Jizhou Huang, Yang Zhou et al. | 360 | cites it |
| 2024-11-26 | [Trustworthy federated learning: privacy, security, and beyond](https://doi.org/10.1007/s10115-024-02285-2) | [Knowledge and Information Systems](https://scholar4ai.com/journals/knowledge-and-information-systems.md) | Chunlu Chen, Ji Liu, Haowen Tan et al. | 87 | cites it |
| 2024-11-09 | [A systematic review on federated learning system: a new paradigm to machine learning](https://doi.org/10.1007/s10115-024-02257-6) | [Knowledge and Information Systems](https://scholar4ai.com/journals/knowledge-and-information-systems.md) | Rajesh Kumar Chaudhary, Ravinder Kumar, Nitin Saxena | 21 | cites it |
| 2023-11-13 | [Exploring the distributed learning on federated learning and cluster computing via convolutional neural networks](https://doi.org/10.1007/s00521-023-09160-1) | [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | Jia-Wei Chang, Jason Chi-Shun Hung, Ting-Hong Chu | 4 | cites it |
| 2025-12-24 | [Parasite: planting durable backdoors against continual federated model fusion via discriminative feature pushing-pulling](https://doi.org/10.1016/j.inffus.2025.104081) | [Information Fusion](https://scholar4ai.com/journals/information-fusion.md) | Tong Chen, Bowen Du, Jiejie Zhao et al. | 1 | cites it |
| 2026-05-09 | [Federated learning with context-aware client collaboration: Challenges, advances, and open problems](https://doi.org/10.1016/j.inffus.2026.104446) | [Information Fusion](https://scholar4ai.com/journals/information-fusion.md) | Soroush Ziaeinejad, Saeed Samet | 0 | 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). FederatedAI/FATE: papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/github-federatedai-fate

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

License: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/).