Federated AI Ecosystem
@federatedai • Collaborative Learning and Knowledge Transfer with Data Protection. Use this route to separate flagship concentration from portfolio breadth before you treat a publisher as broadly strong.
Portfolio concentration
84%
Top three share
Shows whether the organization is driven by one breakout repo or several visible projects.
Breadth
23 repos
Visible snapshot
1 repositories updated in the last 90 days.
Leading language
Python
Portfolio mix
Python (10), Java (5), Unknown (4)
Average size
385
Stars per repository
Useful for distinguishing one flagship-heavy publisher from a repeatable portfolio.
Updated: 2024-12-04(630d ago)GitHub API fallback23 repositories
84%
of the visible star count comes from this organization's top three repositories.
385
stars per repository in this same snapshot.
Python
is the most common language here, with 1 repositories updated in the last 90 days.
Why this rank
This organization stands out because one flagship repo drives 69% of its visible star count.
Flagship share 69%Breakout repo: FATE
Organization pages work best when you separate portfolio breadth from flagship concentration. In Federated AI Ecosystem's case, the visible top three repositories account for about 84% of total stars in this snapshot, which helps explain whether the organization is known for one breakout project or for a broader repeatable portfolio.
The dominant language mix here is Python (10), Java (5), Unknown (4). That makes this page useful not just for popularity checks, but also for seeing what technical shape an organization's public ecosystem actually has.
Top Repositories
| # | Repository | Language | Stars |
|---|---|---|---|
| 1 | federatedai/FATE An Industrial Grade Federated Learning Framework | Python | 6.1K |
| 2 | federatedai/Practicing-Federated-Learning | Python | 895 |
| 3 | federatedai/KubeFATE Manage federated learning workload using cloud native technologies. | Go | 435 |
| 4 | federatedai/FATE-LLM Federated Learning for LLMs. | Python | 270 |
| 5 | federatedai/eggroll A Simple High Performance Computing Framework for [Federated] Machine Learning | Java | 253 |
| 6 | federatedai/research | Python | 179 |
| 7 | federatedai/FATE-Serving A scalable, high-performance serving system for federated learning models | Java | 143 |
| 8 | federatedai/DOC-CHN FedAI 社区中文文档 | Python | 139 |
| 9 | federatedai/FedVision Federated Computer Vision Engine | Python | 113 |
| 10 | federatedai/FATE-Board FATE's Visualization Toolkit | Java | 109 |
| 11 | federatedai/FATE-Flow Solution for deploying and managing end-to-end federated learning workflows | Python | 58 |
| 12 | federatedai/FedRec | Python | 42 |
| 13 | federatedai/FATE-Cloud Infrastructure for building and managing Federated Data Collaboration Network | Java | 31 |
| 14 | federatedai/FATE-Community How FedAI community is running | 26 | |
| 15 | federatedai/AnsibleFATE | Shell | 24 |
| 16 | federatedai/FedLCM A web application that manages lifecycles of federated learning federations. | Go | 21 |
| 17 | federatedai/InterOp Repository for Interoperability of FATE | 12 | |
| 18 | federatedai/FATE-Builder A release packing tool for FATE | Jupyter Notebook | 9 |
| 19 | federatedai/highflip-fate-adapter FATE Adapter for HighFlip | Java | 3 |
| 20 | federatedai/FATE-Client | Python | 3 |
| 21 | federatedai/FATE-Test | Python | 1 |
| 22 | federatedai/benchmark_kits | 1 | |
| 23 | federatedai/fate-artwork Fate Project Related Logos and Artwork | 0 |
Next step after the organization read
Open a flagship repository, compare a couple of portfolio leaders, or return to the organization map when you want a broader concentration read.
Learn and methodology
Keep trust-building context reachable, but behind the first data read instead of ahead of it.
How to read this organization snapshot
Total stars are useful as a discovery signal, but they do not tell you whether a team maintains every repository equally. Pair this page with release cadence, maintainer activity, and the flagship concentration shown above before making adoption decisions.
For broader background on GitStar's ranking logic and editorial guidance, see Methodology & Editorial Standards.