Federated Learning Application Runtime Environment
Project description
NVIDIA Federated Learning Application Runtime Environment
NVFlare enables researchers to collaborate and build AI models without sharing private data.
NVFlare is a standalone python library designed to enable federated learning amongst different parties using their local secure protected data for client-side training, at the same time includes capabilities to coordinate and exchange progressing of results across all sites to achieve better global model while still preserving data privacy. The participating clients can be in any part of the world.
NVFlare builds on a flexible and modular architecture and is abstracted through APIs allowing developers & researchers to customize their implementation of functional learning components in a Federated Learning paradigm.
Learn more - NVFlare.
Installation
To install the current release, you can simply run:
pip install nvflare
Third party license
torch: https://github.com/pytorch/pytorch/blob/master/LICENSE
pyyaml: https://github.com/yaml/pyyaml/blob/master/LICENSE (MIT)
grpcio: https://pypi-hypernode.com/project/grpcio/ (Apache 2.0)
google-api-python-client: https://github.com/googleapis/google-api-python-client/blob/master/LICENSE (Apache 2.0)
psutil: https://github.com/giampaolo/psutil/blob/master/LICENSE (BSD 3-Clause)
cryptography: https://github.com/pyca/cryptography/blob/main/LICENSE (BSD, Apache and PSF)
rich: https://github.com/willmcgugan/rich/blob/master/LICENSE (MIT)
tenseal: https://github.com/OpenMined/TenSEAL/blob/master/LICENSE (Apache 2.0)
License
By installing the software, you agree to the license terms.
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distributions
Built Distribution
File details
Details for the file nvflare-1.0.1-py3-none-any.whl.
File metadata
- Download URL: nvflare-1.0.1-py3-none-any.whl
- Upload date:
- Size: 510.5 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/3.4.1 importlib_metadata/4.0.1 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.60.0 CPython/3.8.5
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | de8bbb84de1edc9fdacfb73934522f68fc0092ba3089b8af208be7101bb50a33 |
|
| MD5 | d72423ff8cba9da0f8afbbc7b6a9c30f |
|
| BLAKE2b-256 | 4ecd2ef6287d5405d0cbdebaa4243fa6a6f7c3d6c069b3808273ce5b20d52c4d |
