nvflare

NVIDIA Federated Learning Application Runtime Environment

Science Score: 64.0%

This score indicates how likely this project is to be science-related based on various indicators:

✓
CITATION.cff file
Found CITATION.cff file
✓
codemeta.json file
Found codemeta.json file
✓
.zenodo.json file
Found .zenodo.json file
○
DOI references
✓
Academic publication links
Links to: arxiv.org
✓
Committers with academic emails
1 of 44 committers (2.3%) from academic institutions
○
Institutional organization owner
○
JOSS paper metadata
○
Scientific vocabulary similarity
Low similarity (13.0%) to scientific vocabulary

Keywords

decentralized federated-analytics federated-computing federated-learning pet privacy-protection python

Keywords from Contributors

monai medical-image-processing medical-image-computing healthcare-imaging agents interactive distributed transformers graph-computation observability

Last synced: 6 months ago · JSON representation ·

Repository

NVIDIA Federated Learning Application Runtime Environment

Basic Info

Host: GitHub
Owner: NVIDIA
License: apache-2.0
Language: Python
Default Branch: main
Homepage: https://nvidia.github.io/NVFlare/
Size: 121 MB

Statistics

Stars: 781
Watchers: 19
Forks: 209
Open Issues: 29
Releases: 80

Topics

decentralized federated-analytics federated-computing federated-learning pet privacy-protection python

Created over 4 years ago · Last pushed 6 months ago

Metadata Files

Readme Contributing License Code of conduct Citation Codeowners

NVIDIA FLARE

NVIDIA FLARE (NVIDIA Federated Learning Application Runtime Environment) is a domain-agnostic, open-source, extensible Python SDK that allows researchers and data scientists to adapt existing ML/DL workflows to a federated paradigm. It enables platform developers to build a secure, privacy-preserving offering for a distributed multi-party collaboration.

Features

FLARE is built on a componentized architecture that allows you to take federated learning workloads from research and simulation to real-world production deployment.

Application Features * Support both deep learning and traditional machine learning algorithms (eg. PyTorch, TensorFlow, Scikit-learn, XGBoost etc.) * Support horizontal and vertical federated learning * Built-in Federated Learning algorithms (e.g., FedAvg, FedProx, FedOpt, Scaffold, Ditto, etc.) * Support multiple server and client-controlled 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, private set intersection (PSI)

From Simulation to Real-World * FLARE Client API to transition seamlessly from ML/DL to FL with minimal code changes * Simulator and POC mode for rapid development and prototyping * Fully customizable and extensible components with modular design * Deployment on cloud and on-premise * Dashboard for project management and deployment * Security enforcement through federated authorization and privacy policy * Built-in support for system resiliency and fault tolerance

Take a look at NVIDIA FLARE Overview for a complete overview, and What's New for the lastest changes.

Installation

To install the current release: $ python -m pip install nvflare

For detailed installation please refer to NVIDIA FLARE installation.

Getting Started

To get started, refer to getting started documentation
Structured, self-paced learning is available through curated tutorials and training paths on the website.
- DLI courses:
- https://learn.nvidia.com/courses/course-detail?course_id=course-v1:DLI+S-FX-28+V1
- https://learn.nvidia.com/courses/course-detail?course_id=course-v1:DLI+S-FX-29+V1
visit developer portal https://developer.nvidia.com/flare

Community

We welcome community contributions! Please refer to the contributing guidelines for more details.

Ask and answer questions, share ideas, and engage with other community members at NVFlare Discussions.

Related Talks and Publications

Take a look at our growing list of talks and publications, and technical blogs related to NVIDIA FLARE.

License

NVIDIA FLARE is released under an Apache 2.0 license.

Owner

Name: NVIDIA Corporation
Login: NVIDIA
Kind: organization
Location: 2788 San Tomas Expressway, Santa Clara, CA, 95051

Website: https://nvidia.com
Repositories: 342
Profile: https://github.com/NVIDIA

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

Committers

Last synced: 9 months ago

All Time

Total Commits: 2,182
Total Committers: 44
Avg Commits per committer: 49.591
Development Distribution Score (DDS): 0.753

Past Year

Commits: 570
Committers: 25
Avg Commits per committer: 22.8
Development Distribution Score (DDS): 0.814

Top Committers

Name	Email	Commits
Yuan-Ting Hsieh (謝沅廷)	y**h@n**m	538
Yuhong Wen	y**w@n**m	259
Isaac Yang	i**y@n**m	240
Chester Chen	5****n	230
Holger Roth	6****h	176
nvkevlu	5****u	161
Sean Yang	s**4@g**m	145
Zhihong Zhang	1****z	127
Yan Cheng	5****v	106
Ziyue Xu	7****7	98
dependabot[bot]	4****]	17
Zhijin	z**l@n**m	15
Arun Patole	a**e@n**m	7
Mohammad Adil	m**l@n**m	7
YanxuanLiu	1****u	6
Kris Kersten	3****n	5
Francesco Farina	f**1@g**m	4
Peixin	p**i@n**u	4
Dirk Petersen	d****n	3
Hao-Wei Pang	4****g	3
Can Zhao	6****o	2
Jiaxin Shan	s**n@g**m	2
Tal Einat	5****t	2
Wenqi Li	8****i	2
Yiheng Wang	6****v	2
eordentlich	3****h	2
falibabaei	6****i	2
Nicolas Pannetier	n**r@f**o	1
O L	1****i	1
Pengfei Guo	3****f	1
and 14 more...

Committer Domains (Top 20 + Academic)

nvidia.com: 6 flywheel.io: 1 nyu.edu: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time

Total issues: 122
Total pull requests: 2,703
Average time to close issues: 4 months
Average time to close pull requests: 5 days
Total issue authors: 62
Total pull request authors: 41
Average comments per issue: 2.5
Average comments per pull request: 1.72
Merged pull requests: 2,158
Bot issues: 0
Bot pull requests: 52

Past Year

Issues: 42
Pull requests: 1,103
Average time to close issues: 20 days
Average time to close pull requests: 3 days
Issue authors: 31
Pull request authors: 27
Average comments per issue: 1.29
Average comments per pull request: 1.79
Merged pull requests: 816
Bot issues: 0
Bot pull requests: 48

View more stats

Top Authors

Issue Authors

holgerroth (19)
dirkpetersen (12)
falibabaei (10)
YuanTingHsieh (7)
taleinat (4)
Nintorac (3)
avelinoapheris (3)
pygabc1 (2)
FancyXun (2)
Writam18 (2)
yizhihenpidehou (2)
Nicholas-B1 (2)
hwpang (2)
mingxin-zheng (2)
ductrong5x5 (2)

Pull Request Authors

YuanTingHsieh (641)
SYangster (307)
yhwen (301)
holgerroth (256)
yanchengnv (245)
nvidianz (185)
chesterxgchen (176)
IsaacYangSLA (158)
ZiyueXu77 (143)
nvkevlu (133)
dependabot[bot] (52)
zhijinl (19)
apatole (11)
pxLi (9)
hwpang (8)

Top Labels

Issue Labels

bug (55) enhancement (9) question (6) Feature request (5) core (1) cicd (1)

Pull Request Labels

cicd (87) documentation (72) examples (61) dependencies (52) enhancement (39) bug (38) javascript (35) python (14) core (3) research (1)

Packages

Total packages: 4
Total downloads:
- pypi 11,244 last-month
Total docker downloads: 85

Total dependent packages: 2
(may contain duplicates)
Total dependent repositories: 44
(may contain duplicates)
Total versions: 171
Total maintainers: 2
Total advisories: 4

pypi.org: nvflare

Federated Learning Application Runtime Environment

Homepage: https://github.com/NVIDIA/NVFlare
Documentation: https://nvflare.readthedocs.io/
License: Apache Software License
Latest release: 2.6.2
published 7 months ago

Versions: 116
Dependent Packages: 2
Dependent Repositories: 43
Downloads: 11,073 Last month
Docker Downloads: 51

Rankings

Dependent repos count: 2.2%

Stargazers count: 2.9%

Average: 3.8%

Downloads: 4.1%

Forks count: 4.2%

Docker downloads count: 4.3%

Dependent packages count: 4.8%

Maintainers (2)

nvidia call-151

Advisories (4)

Last synced: 6 months ago

pypi.org: nvflare-nightly

Federated Learning Application Runtime Environment

Homepage: https://github.com/NVIDIA/NVFlare
Documentation: https://nvflare-nightly.readthedocs.io/
License: Other/Proprietary License
Latest release: 1.1.0.dev211220
published about 4 years ago

Versions: 22
Dependent Packages: 0
Dependent Repositories: 1
Downloads: 71 Last month

Rankings

Stargazers count: 2.9%

Forks count: 4.2%

Dependent packages count: 10.1%

Average: 12.5%

Dependent repos count: 21.5%

Downloads: 23.8%

Maintainers (1)

call-151

Last synced: 6 months ago

pypi.org: monai-nvflare

MONAI NVIDIA FLARE integration

Homepage: https://github.com/NVIDIA/NVFlare
Documentation: https://monai-nvflare.readthedocs.io/
License: Apache Software License
Latest release: 0.3.1
published 11 months ago

Versions: 18
Dependent Packages: 0
Dependent Repositories: 0
Downloads: 79 Last month
Docker Downloads: 34

Rankings

Stargazers count: 3.5%

Forks count: 4.8%

Dependent packages count: 6.6%

Average: 14.3%

Downloads: 25.8%

Dependent repos count: 30.6%

Maintainers (1)

call-151

Last synced: 6 months ago

pypi.org: nvflare-light

Federated Learning Application Runtime Environment

Homepage: https://github.com/NVIDIA/NVFlare
Documentation: https://nvflare-light.readthedocs.io/
License: Apache Software License
Latest release: 2.5.1
published over 1 year ago

Versions: 15
Dependent Packages: 0
Dependent Repositories: 0
Downloads: 21 Last month

Rankings

Dependent packages count: 10.1%

Downloads: 18.5%

Average: 28.5%

Dependent repos count: 56.9%

Maintainers (1)

call-151

Last synced: 6 months ago

Dependencies

setup.py pypi

PyYAML *
cryptography >=36.0.0
flask *
google-api-python-client *
grpcio *
gunicorn *
numpy *
psutil *
tenseal ==0.3.0

.github/workflows/blossom-ci.yml actions

NVIDIA/blossom-action main composite
actions/checkout v2 composite

.github/workflows/codeql.yml actions

actions/checkout v3 composite
github/codeql-action/analyze v2 composite
github/codeql-action/autobuild v2 composite
github/codeql-action/init v2 composite

.github/workflows/premerge.yml actions

actions/checkout v3 composite
actions/setup-python v4 composite

docker/Dockerfile docker

python 3.8 build

integration/monai/setup.py pypi

monai >=1.0.1

.github/workflows/markdown-links-check.yml actions

actions/checkout master composite
gaurav-nelson/github-action-markdown-link-check 1.0.15 composite

examples/advanced/brats18/configs/brats_central/meta.json cpan

examples/advanced/brats18/configs/brats_fedavg/meta.json cpan

examples/advanced/brats18/configs/brats_fedavg_dp/meta.json cpan

examples/advanced/cifar10/cifar10-real-world/jobs/cifar10_fedavg_he/meta.json cpan

examples/advanced/cifar10/cifar10-real-world/jobs/cifar10_fedavg_stream_tb/meta.json cpan

examples/advanced/cifar10/cifar10-sim/jobs/cifar10_central/meta.json cpan

examples/advanced/cifar10/cifar10-sim/jobs/cifar10_fedavg/meta.json cpan

examples/advanced/cifar10/cifar10-sim/jobs/cifar10_fedopt/meta.json cpan

examples/advanced/cifar10/cifar10-sim/jobs/cifar10_fedprox/meta.json cpan

examples/advanced/cifar10/cifar10-sim/jobs/cifar10_scaffold/meta.json cpan

examples/advanced/experiment-tracking/mlflow/jobs/hello-pt-mlflow/meta.json cpan

examples/advanced/experiment-tracking/mlflow/jobs/hello-pt-tb-mlflow/meta.json cpan

examples/advanced/experiment-tracking/tensorboard/jobs/tensorboard-streaming/meta.json cpan

examples/advanced/experiment-tracking/wandb/jobs/hello-pt-wandb/meta.json cpan

examples/advanced/federated-policies/jobs/job1/meta.json cpan

examples/advanced/federated-policies/jobs/job2/meta.json cpan

examples/advanced/federated-policies/jobs/job3/meta.json cpan

examples/advanced/federated-policies/jobs/job4/meta.json cpan

examples/advanced/federated-policies/jobs/job5/meta.json cpan

examples/advanced/federated-statistics/df_stats/jobs/df_stats/meta.json cpan

examples/advanced/federated-statistics/image_stats/jobs/image_stats/meta.json cpan

examples/advanced/fl_hub/jobs/numpy-cross-val/meta.json cpan

examples/advanced/gnn-pt/jobs/gnn-pt/meta.json cpan

examples/advanced/nlp-ner/jobs/bert_ncbi/meta.json cpan

examples/advanced/nlp-ner/jobs/gpt2_ncbi/meta.json cpan

examples/advanced/prostate/prostate_2D/job_configs/prostate_central/meta.json cpan

examples/advanced/prostate/prostate_2D/job_configs/prostate_ditto/meta.json cpan

examples/advanced/prostate/prostate_2D/job_configs/prostate_fedavg/meta.json cpan

examples/advanced/prostate/prostate_2D/job_configs/prostate_fedprox/meta.json cpan

examples/advanced/prostate/prostate_3D/job_configs/prostate_central/meta.json cpan

examples/advanced/prostate/prostate_3D/job_configs/prostate_ditto/meta.json cpan

examples/advanced/prostate/prostate_3D/job_configs/prostate_fedavg/meta.json cpan

examples/advanced/prostate/prostate_3D/job_configs/prostate_fedprox/meta.json cpan

examples/advanced/psi/user_email_match/jobs/user_email_match/meta.json cpan

examples/advanced/random_forest/jobs/random_forest_base/meta.json cpan

examples/advanced/sklearn-kmeans/jobs/sklearn_kmeans_base/meta.json cpan

examples/advanced/sklearn-linear/jobs/sklearn_linear_base/meta.json cpan

examples/advanced/sklearn-svm/jobs/sklearn_svm_base/meta.json cpan

examples/advanced/vertical_federated_learning/cifar10-splitnn/jobs/cifar10_psi/meta.json cpan

examples/advanced/vertical_federated_learning/cifar10-splitnn/jobs/cifar10_splitnn/meta.json cpan

examples/advanced/vertical_xgboost/jobs/vertical_xgb/meta.json cpan

examples/advanced/vertical_xgboost/jobs/vertical_xgb_psi/meta.json cpan

examples/advanced/xgboost/histogram-based/jobs/base/meta.json cpan

examples/advanced/xgboost/tree-based/jobs/bagging_base/meta.json cpan

examples/advanced/xgboost/tree-based/jobs/cyclic_base/meta.json cpan

examples/hello-world/hello-ccwf/jobs/numpy-swcse/meta.json cpan

examples/hello-world/hello-cyclic/jobs/hello-cyclic/meta.json cpan

examples/hello-world/hello-numpy-cross-val/jobs/hello-numpy-cross-val/meta.json cpan

examples/hello-world/hello-numpy-sag/jobs/hello-numpy-sag/meta.json cpan

examples/hello-world/hello-pt/jobs/hello-pt/meta.json cpan

examples/hello-world/hello-tf2/jobs/hello-tf2/meta.json cpan

integration/monai/examples/spleen_ct_segmentation_local/jobs/spleen_ct_segmentation_he/meta.json cpan

integration/monai/examples/spleen_ct_segmentation_local/jobs/spleen_ct_segmentation_loc_non_agg/meta.json cpan

integration/monai/examples/spleen_ct_segmentation_local/jobs/spleen_ct_segmentation_local/meta.json cpan

integration/monai/examples/spleen_ct_segmentation_sim/job/meta.json cpan

integration/monai/examples/spleen_ct_segmentation_sim/job_multi_gpu/meta.json cpan

integration/monai/examples/spleen_ct_segmentation_sim/job_stats/meta.json cpan

integration/nemo/examples/prompt_learning/jobs/templates/meta.json cpan

integration/nemo/examples/supervised_fine_tuning/jobs/templates/meta.json cpan

research/auto-fed-rl/jobs/cifar10_autofedrl/meta.json cpan

research/condist-fl/jobs/condist/meta.json cpan

research/fed-ce/jobs/fedce_prostate/meta.json cpan

research/fed-sm/jobs/fedsm_prostate/meta.json cpan

research/one-shot-vfl/jobs/cifar10_oneshotVFL/meta.json cpan

research/one-shot-vfl/jobs/cifar10_psi/meta.json cpan

research/quantifying-data-leakage/jobs/app_template/meta.json cpan

tests/integration_test/data/apps/cross_val/meta.json cpan

tests/integration_test/data/apps/cross_val_one_client/meta.json cpan

tests/integration_test/data/apps/cyclic/meta.json cpan

tests/integration_test/data/apps/filters/meta.json cpan

tests/integration_test/data/apps/global_model_eval/meta.json cpan

tests/integration_test/data/apps/np_sag/meta.json cpan

tests/integration_test/data/apps/np_sag_weights_diff/meta.json cpan

tests/integration_test/data/apps/pt_init_client/meta.json cpan

tests/integration_test/data/apps/pt_use_name/meta.json cpan

tests/integration_test/data/apps/pt_use_path/meta.json cpan

tests/integration_test/data/apps/sag_exception/meta.json cpan

tests/integration_test/data/apps/slow_job/meta.json cpan

tests/integration_test/data/apps/tb_streaming/meta.json cpan

tests/unit_test/data/jobs/duplicate_clients/meta.json cpan

tests/unit_test/data/jobs/duplicate_server/meta.json cpan

tests/unit_test/data/jobs/invalid_resource_spec_data_type/meta.json cpan

tests/unit_test/data/jobs/mandatory_not_met/meta.json cpan

tests/unit_test/data/jobs/min_clients_value_range/meta.json cpan

tests/unit_test/data/jobs/missing_app/meta.json cpan

tests/unit_test/data/jobs/missing_client_config/meta.json cpan

tests/unit_test/data/jobs/missing_server_config/meta.json cpan

tests/unit_test/data/jobs/missing_server_in_deployment/meta.json cpan

tests/unit_test/data/jobs/no_deployment/meta.json cpan

tests/unit_test/data/jobs/not_enough_clients/meta.json cpan

tests/unit_test/data/jobs/valid_job/meta.json cpan

tests/unit_test/data/jobs/valid_job_deployment_all_idle/meta.json cpan

examples/advanced/brats18/plot-requirements.txt pypi

matplotlib *
seaborn *
tensorflow *

nvflare

Science Score: 64.0%

Keywords

Keywords from Contributors

Repository

Basic Info

Statistics

Topics

Metadata Files

README.md

NVIDIA FLARE

Features

Installation

Getting Started

Community

Related Talks and Publications

License

Owner

Citation (CITATION.cff)

Committers

All Time

Past Year

Top Committers

Committer Domains (Top 20 + Academic)

Issues and Pull Requests

All Time

Past Year

Top Authors

Issue Authors

Pull Request Authors

Top Labels

Issue Labels

Pull Request Labels

Packages

pypi.org: nvflare

Rankings

Maintainers (2)

Advisories (4)

pypi.org: nvflare-nightly

Rankings

Maintainers (1)

pypi.org: monai-nvflare

Rankings

Maintainers (1)

pypi.org: nvflare-light

Rankings

Maintainers (1)

Dependencies