phamxuansang241/Secure-Federated-Learning
Secure Federated Learning Framework with Encryption Aggregation and Integer Encoding Method.
This framework helps organizations train machine learning models using data stored across multiple decentralized systems without ever sharing the raw data. It takes secure data from different sources and produces a unified, privacy-preserving machine learning model. This is designed for data scientists, security engineers, or IT managers who need to build models while adhering to strict data privacy or regulatory requirements.
No commits in the last 6 months.
Use this if you need to train machine learning models on sensitive data distributed across various locations, such as customer records, medical information, or proprietary business data, without centralizing or exposing that data.
Not ideal if your data is already centralized, you are training models in a non-sensitive environment, or you require maximum model performance at the expense of privacy.
Stars
10
Forks
3
Language
Python
License
MIT
Category
Last pushed
Jul 01, 2024
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/phamxuansang241/Secure-Federated-Learning"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
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...