testnvflare
Science Score: 44.0%
This score indicates how likely this project is to be science-related based on various indicators:
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✓CITATION.cff file
Found CITATION.cff file -
✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
Found .zenodo.json file -
○DOI references
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○Academic publication links
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○Academic email domains
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○Institutional organization owner
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○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (12.7%) to scientific vocabulary
Repository
Basic Info
- Host: GitHub
- Owner: vestata
- License: apache-2.0
- Language: Python
- Default Branch: testnvflare
- Size: 25.5 MB
Statistics
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
- Releases: 0
Metadata Files
README.md
NVIDIA Federated Learning Application Runtime Environment
NVIDIA FLARE is a domain-agnostic, open-source, extensible SDK that allows researchers and data scientists to adapt existing ML/DL workflows(PyTorch, TensorFlow, Scikit-learn, XGBoost etc.) to a federated paradigm. It enables platform developers to build a secure, privacy-preserving offering for a distributed multi-party collaboration.
NVIDIA FLARE is built on a componentized architecture that allows you to take federated learning workloads from research and simulation to real-world production deployment. Key components include:
- Support both deep learning and traditional machine algorithms
- Support horizontal and vertical federated learning
- Built-in FL algorithms (e.g., FedAvg, FedProx, FedOpt, Scaffold, Ditto )
- Support multiple training workflows (e.g., scatter & gather, cyclic) and validation workflows (global model evaluation, cross-site validation)
- Support both data analytics (federated statistics) and machine learning lifecycle management
- Privacy preservation with differential privacy, homomorphic encryption
- Security enforcement through federated authorization and privacy policy
- Easily customizable and extensible
- Deployment on cloud and on premise
- Simulator for rapid development and prototyping
- Dashboard UI for simplified project management and deployment
- Built-in support for system resiliency and fault tolerance
Installation
To install the current release, you can simply run:
$ python3 -m pip install nvflare
Getting started
You can quickly get started using the FL simulator.
A detailed getting started guide is available in the documentation.
Examples and notebook tutorials are located here.
Related talks and publications
For a list of talks, blogs, and publications related to NVIDIA FLARE, see here.
License
NVIDIA FLARE has Apache 2.0 license, as found in LICENSE file.
Owner
- Name: nini
- Login: vestata
- Kind: user
- Repositories: 1
- Profile: https://github.com/vestata
Citation (CITATION.cff)
# Metadata for citation of this software according to the CFF format (https://citation-file-format.github.io/)
#
---
title: "Nvidia FLARE"
abstract: "Nvidia FLARE is an SDK designed to enable federated learning amongst different parties using
their local secure protected data for client-side training, at the same time it includes capabilities to
coordinate and exchange progressing of results across all sites to achieve better global model while
preserving data privacy."
authors:
- name: "Nvidia Corporation"
date-released: 2022-06-29
version: "2.1.2"
identifiers:
- description: "This DOI represents all versions of Nvidia FLARE, and will always resolve to the latest one."
type: doi
value: "10.5281/zenodo.6780567"
license: "Apache-2.0"
doi: 10.5281/zenodo.6780567
repository-code: "https://github.com/NVIDIA/nvflare"
url: "https://nvflare.readthedocs.io"
cff-version: "1.2.0"
message: "If you use this software, please cite it using these metadata."
preferred-citation:
type: article
authors:
- family-names: Roth
given-names: Holger R.
- family-names: Cheng
given-names: Yan
- family-names: Wen
given-names: Yuhong
- family-names: Yang
given-names: Isaac
- family-names: Xu
given-names: Ziyue
- family-names: Hsieh
given-names: Yuan-Ting
- family-names: Kersten
given-names: Kristopher
- family-names: Harouni
given-names: Ahmed
- family-names: Zhao
given-names: Can
- family-names: Lu
given-names: Kevin
- family-names: Zhang
given-names: Zhihong
- family-names: Li
given-names: Wenqi
- family-names: Myronenko
given-names: Andriy
- family-names: Yang
given-names: Dong
- family-names: Yang
given-names: Sean
- family-names: Rieke
given-names: Nicola
- family-names: Quraini
given-names: Abood
- family-names: Chen
given-names: Chester
- family-names: Xu
given-names: Daguang
- family-names: Ma
given-names: Nic
- family-names: Dogra
given-names: Prerna
- family-names: Flores
given-names: Mona
- family-names: Feng
given-names: Andrew
doi: "https://doi.org/10.48550/arXiv.2210.13291"
journal: "IEEE Data Eng. Bull., Vol. 46, No. 1"
month: 3
title: "NVIDIA FLARE: Federated Learning from Simulation to Real-World"
year: 2023
GitHub Events
Total
Last Year
Dependencies
- NVIDIA/blossom-action main composite
- actions/checkout v2 composite
- actions/checkout v3 composite
- github/codeql-action/analyze v2 composite
- github/codeql-action/autobuild v2 composite
- github/codeql-action/init v2 composite
- actions/checkout master composite
- gaurav-nelson/github-action-markdown-link-check 1.0.15 composite
- actions/checkout v3 composite
- actions/setup-python v4 composite
- python 3.8 build
- matplotlib *
- seaborn *
- tensorflow *
- monai *
- nibabel *
- nvflare >=2.3.0
- tensorboard *
- torch *
- torchvision *
- tqdm *
- pandas *
- seaborn *
- tensorflow *
- nvflare >=2.3.0
- tensorboard *
- torch *
- torchvision *
- pandas *
- seaborn *
- tensorflow *
- nvflare >=2.3.0
- tensorboard *
- torch *
- torchvision *
- mlflow *
- nvflare >=2.3.0
- tensorboard *
- torch *
- torchvision *
- nvflare >=2.3.0
- tensorboard *
- nvflare >=2.3.0
- tensorboard *
- wandb *
- nvflare >=2.3.0
- jupyterlab *
- matplotlib *
- numpy *
- nvflare >=2.3.0
- pandas *
- jupyter *
- kaleido *
- matplotlib *
- monai *
- notebook *
- numpy *
- nvflare >=2.3.0
- pandas *
- nvflare >=2.3.0
- scikit_learn ==1.2.2
- torch_geometric ==2.3.1
- tqdm ==4.66.1
- nvflare *
- pandas *
- seqeval *
- tensorboard *
- torch *
- torchvision *
- transformers *
- matplotlib *
- seaborn *
- tensorflow *
- monai *
- nibabel *
- nvflare >=2.3.0
- tensorboard *
- torch *
- torchvision *
- tqdm *