paritybit-ai/XFL
An Efficient and Easy-to-use Federated Learning Framework.
This framework helps organizations collaboratively train machine learning models on their combined datasets without ever directly sharing sensitive raw data. It takes secure datasets from multiple parties and outputs a powerful, jointly-trained model, ensuring data privacy through advanced cryptographic techniques. This is designed for data scientists and machine learning engineers working on projects that require cross-organizational data collaboration while maintaining strict data confidentiality.
Use this if you need to build more accurate AI models by leveraging data from multiple sources or partners, but legal or privacy regulations prevent direct data sharing.
Not ideal if your data is already centralized, not sensitive, or you require absolute transparency of each participant's raw data for auditing purposes.
Stars
43
Forks
12
Language
Python
License
Apache-2.0
Category
Last pushed
Feb 21, 2026
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/paritybit-ai/XFL"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Related frameworks
flwrlabs/flower
Flower: A Friendly Federated AI Framework
JonasGeiping/breaching
Breaching privacy in federated learning scenarios for vision and text
anupamkliv/FedERA
FedERA is a modular and fully customizable open-source FL framework, aiming to address these...
zama-ai/concrete-ml
Concrete ML: Privacy Preserving ML framework using Fully Homomorphic Encryption (FHE), built on...
p2pfl/p2pfl
P2PFL is a decentralized federated learning library that enables federated learning on...