Merge branch 'main' into fix-fobs-lazy-class-loading

README.md

@@ -2,7 +2,7 @@
 
 # NVIDIA FLARE
 
-[Website](https://nvidia.github.io/NVFlare) | [Paper](https://arxiv.org/abs/2210.13291) | [Talks & Blogs](https://nvflare.readthedocs.io/en/main/publications_and_talks.html) | [Research](./research/README.md) | [Documentation](https://nvflare.readthedocs.io/en/main)
+[Website](https://nvidia.github.io/NVFlare) | [Paper](https://arxiv.org/abs/2210.13291) | [Blogs](https://developer.nvidia.com/blog/tag/federated-learning) | [Talks & Papers](https://nvflare.readthedocs.io/en/main/publications_and_talks.html) | [Research](./research/README.md) | [Documentation](https://nvflare.readthedocs.io/en/main)
 
 [![Blossom-CI](https://github.com/NVIDIA/nvflare/workflows/Blossom-CI/badge.svg?branch=main)](https://github.com/NVIDIA/nvflare/actions)
 [![documentation](https://readthedocs.org/projects/nvflare/badge/?version=main)](https://nvflare.readthedocs.io/en/main/?badge=main)

docs/getting_started.rst

@@ -4,4 +4,9 @@
 Getting Started
 ###############
 
-See :ref:`installation`.
+### To get started, take a look at the instructions and code examples on our [**website**](https://nvidia.github.io/NVFlare/).
+
+* For more details, see our getting started `tutorials <https://github.com/NVIDIA/NVFlare/tree/main/examples/getting_started>`__.
+* For more advanced examples and `step-by-step <https://github.com/NVIDIA/NVFlare/tree/main/examples/hello-world/step-by-step>`__ walk-troughs, see our `examples <https://github.com/NVIDIA/NVFlare/tree/main/examples>`__.
+* There are also detailed instructions on how to convert your standalone/centralized training code to `federated learning code. <https://github.com/NVIDIA/NVFlare/tree/main/examples/hello-world/ml-to-fl>`__.
+* If you'd like to write your own NVIDIA FLARE components, a detailed programming guide can be found `here <https://nvflare.readthedocs.io/en/main/programming_guide.html>`__.

docs/publications_and_talks.rst

@@ -9,7 +9,15 @@ including papers using NVIDIA FLARE's predecessor libraries included in the `Cla
 
 Publications: 2024
 ------------------
-* **2024-02** `Empowering Federated Learning for Massive Models with NVIDIA FLARE <https://arxiv.org/abs/2402.07792>`__ (Accepted to `FL@FM-TheWebConf'24 <https://federated-learning.org/fl@fm-www-2024>`__))
+* **2024-12** `C-FedRAG: A Confidential Federated Retrieval-Augmented Generation System <https://arxiv.org/abs/2412.13163>`__ (preprint)
+* **2024-11** `Toward a tipping point in federated learning in healthcare and life sciences <https://www.sciencedirect.com/science/article/pii/S2666389924002368>`__ (`Patterns, Volume 5, Issue 11, 2024, <https://www.sciencedirect.com/journal/patterns>`__)
+* **2024-07** `FedBPT: Efficient Federated Black-box Prompt Tuning for Large Language Models <https://arxiv.org/abs/2310.01467>`__ (`ICML 2024 <https://icml.cc/virtual/2024/poster/34753>`__)
+* **2024-07** `Fair evaluation of federated learning algorithms for automated breast density classification: The results of the 2022 ACR-NCI-NVIDIA federated learning challenge <https://arxiv.org/abs/2405.14900>`__ (`Medical Image Analysis, Volume 95, July 2024 <https://www.sciencedirect.com/science/article/abs/pii/S1361841524001312>`__)
+* **2024-07** `Easy and Scalable Federated Learning in the Age of Large Language Models with NVIDIA FLARE <https://federated-learning.org/fl@fm-icme-2024/HRoth.pdf>`__ (`FL@FM-ICME'24 <https://federated-learning.org/fl@fm-icme-2024/#k3>`__)
+* **2024-05** `Federated Learning Privacy: Attacks, Defenses, Applications, and Policy Landscape - A Survey <https://arxiv.org/abs/2405.03636>`__ (preprint)
+* **2024-05** `Supercharging Federated Learning with Flower and NVIDIA FLARE <https://arxiv.org/abs/2407.00031>`__ (Presented at `FL@FM-IJCAI <https://federated-learning.org/fl@fm-ijcai-2024>`__ In preparation for Lecture Notes in AI)
+* **2024-05** `An in-depth evaluation of federated learning on biomedical natural language processing for information extraction <https://www.nature.com/articles/s41746-024-01126-4>`__ (`Nature Digital Medicine 7, 127, 2024 <https://www.nature.com/npjdigitalmed>`__)
+* **2024-02** `Empowering Federated Learning for Massive Models with NVIDIA FLARE <https://arxiv.org/abs/2402.07792>`__ (Presented at `FL@FM-TheWebConf'24 <https://federated-learning.org/fl@fm-www-2024>`__, `Springer Book Chapter <https://link.springer.com/book/9783031788406#about-this-book>`__))
 
 Publications: 2023
 ------------------
@@ -21,7 +29,6 @@ Publications: 2023
 
 Publications: 2022
 ------------------
-* **2022-11** `Federated Learning with Azure Machine Learning <https://www.youtube.com/watch?v=aTj4AqbCWEA>`__ (Video)
 * **2022-10** `Auto-FedRL: Federated Hyperparameter Optimization for Multi-institutional Medical Image Segmentation <https://arxiv.org/abs/2203.06338>`__ (`ECCV 2022 <https://eccv2022.ecva.net/>`__)
 * **2022-10** `Joint Multi Organ and Tumor Segmentation from Partial Labels Using Federated Learning <https://link.springer.com/chapter/10.1007/978-3-031-18523-6_6>`__ (`DeCaF @ MICCAI 2022 <https://decaf-workshop.github.io/decaf-2022/>`__)
 * **2022-10** `Split-U-Net: Preventing Data Leakage in Split Learning for Collaborative Multi-modal Brain Tumor Segmentation <https://arxiv.org/abs/2208.10553>`__ (`DeCaF @ MICCAI 2022 <https://decaf-workshop.github.io/decaf-2022/>`__)
@@ -52,6 +59,8 @@ NVIDIA FLARE related blogs and other media.
 
 Blogs & Videos: 2024
 --------------------
+* **2024-04** `Differential Privacy and Federated Learning for Medical Data <https://towardsdatascience.com/differential-privacy-and-federated-learning-for-medical-data-0f2437d6ece9>`__ (Roche Technical Blog)
+* **2024-03** `Announcing NVIDIA and Flower Collaboration <https://flower.ai/blog/2024-03-15-announcing-nvidia-and-flower-collaboration/>`__ (Flower Blog)
 * **2024-03** `Turning Machine Learning to Federated Learning in Minutes with NVIDIA FLARE 2.4 <https://developer.nvidia.com/blog/turning-machine-learning-to-federated-learning-in-minutes-with-nvidia-flare-2-4/>`__ (NVIDIA Technical Blog)
 * **2024-02** `Scalable Federated Learning with NVIDIA FLARE for Enhanced LLM Performance <https://developer.nvidia.com/blog/scalable-federated-learning-with-nvidia-flare-for-enhanced-llm-performance/>`__ (NVIDIA Technical Blog)
 
@@ -66,7 +75,6 @@ Blogs & Videos: 2023
 
 Blogs & Videos: 2022
 --------------------
-
 * **2022-10** `Federated Learning from Simulation to Production with NVIDIA FLARE <https://developer.nvidia.com/blog/federated-learning-from-simulation-to-production-with-nvidia-flare/?ncid=so-nvsh-705336#cid=ix11_so-nvsh_en-us>`__ (NVIDIA Technical Blog)
 * **2022-08** `Using Federated Learning to Bridge Data Silos in Financial Services <https://developer.nvidia.com/blog/using-federated-learning-to-bridge-data-silos-in-financial-services/>`__ (NVIDIA Technical Blog)
 * **2022-06** `Experimenting with Novel Distributed Applications Using NVIDIA Flare 2.1 <https://developer.nvidia.com/blog/experimenting-with-novel-distributed-applications-using-nvidia-flare-2-1/>`__ (NVIDIA Technical Blog)
@@ -94,7 +102,9 @@ Recent talks and Webinars covering federated learning research and NVIDIA FLARE.
 
 Talks: 2024
 -----------
-* **2024-03** `Empowering Federated Learning for Massive Models with NVIDIA FLARE <https://youtu.be/-P2ML9Kw0FA>`__ (`SFBigAnalytics Meetup <https://www.meetup.com/sf-big-analytics/?_cookie-check=Efm7MGh7mO4YiV8A>`__)
+* **2024-12** `Real-world Federated Learning with NVIDIA FLARE <https://ucsf.zoom.us/rec/share/ncALLA6B583mh7_Lrj4aPgA-qsonqVq5-Ufi5XZCMm6vH-uK5nVYXyVlsexE5KbC.LAYkmvF-sm-yYjTB?startTime=1733957588000>`__ [Passcode: !Ms8Tw.u8H] (`UCSF Biostatistics and Bioinformatics Seminar <https://epibiostat.ucsf.edu/events/real-world-federated-learning-nvidia-flare>`__)
+* **2024-04** `Federated Learning: Towards Real-world Studies <hhttps://youtu.be/3ELbZYwy3bI?t=2474>`__ (`SFBigAnalytics Meetup <https://www.meetup.com/sf-big-analytics/events/300302421>`__)
+* **2024-03** `Empowering Federated Learning for Massive Models with NVIDIA FLARE <https://youtu.be/-P2ML9Kw0FA>`__ (`SFBigAnalytics Meetup <https://www.meetup.com/sf-big-analytics/events/299337374>`__)
 
 Talks: 2023
 -----------
@@ -103,6 +113,7 @@ Talks: 2023
 
 Talks: 2022
 -----------
+* **2022-11** `Federated Learning with Azure Machine Learning <https://www.youtube.com/watch?v=aTj4AqbCWEA>`__ (Microsoft Developer Video)
 * **2022-10** `Modern Tools for Collaborative Medical Image Analysis <https://drive.google.com/file/d/1hmlyG7g1SU8vhQ5wdTFhkqFO9Ty8BiYG/view?usp=sharing>`__ (`Keynote - DART @ MICCAI 2022 <https://sites.google.com/view/dart2022/home?authuser=0>`__) 
 * **2022-07** `NVIDIA FLARE Tutorial for Beginners <https://www.youtube.com/watch?v=8x7oY3xAgek&t=11s&ab_channel=NVIDIADeveloper>`__ (United Imaging Meetup)
 * **2022-07** `Techniques and Tools for Collaborative Development of AI Models across Institutes <https://www.bilibili.com/video/BV1y14y147nc/?spm_id_from=333.337.search-card.all.click>`__ (`VALSE Webinar <http://valser.org/article-572-1.html>`__)

research/CONTRIBUTING.md

@@ -0,0 +1,70 @@
+# Research Directory
+This research directory is the place to host various research work from the community on Federated learning
+leveraging NVIDIA FLARE. **The code will not be maintained by NVIDIA FLARE team**, but will require Pull Request
+approval process. 
+
+## License
+By providing the code in NVFLARE repository, you will grant the research project in NVIDIA repo to be released under Apache v2 License or equivalent open source license.
+
+## Requirements
+Each research project should create a subdirectory with the following requirements.
+
+* Subdirectory name must be in ASCII string, all in lower, kebab-case, and no longer than 35 characters long
+* Each project should include
+  * README.md -- document must include
+    * Objective 
+    * Background
+    * Description
+    * Setup
+    * Steps to run the code 
+    * Data download and preparation (if applicable)
+    * Expected results
+  * Jobs-folder including configurations and optional custom code
+  * All code should be in runnable condition, i.e., no broken code
+  * License file
+  * Requirements file listing all dependencies, including the NVFLARE version used
+
+## Example
+```
+sample_research$ 
+.
+├── jobs
+    └── job1
+           ├── app_server
+                   ├── config
+                           └── config_fed_server.json
+                   └── custom
+                        └── sample_controller.py
+           └── app_client
+                   ├── config
+                           └── config_fed_client.json
+                   └── custom
+                        └── sample_executor.py
+           └── meta.json
+└── README.md
+└── LICENSE
+└── requirements.txt
+```
+
+## Setup
+To run the research code, we recommend using a virtual environment.
+
+### Set up a virtual environment
+```
+python3 -m pip install --user --upgrade pip
+python3 -m pip install --user virtualenv
+```
+(If needed) make all shell scripts executable using
+```
+find . -name ".sh" -exec chmod +x {} \;
+```
+initialize virtual environment.
+```
+python3 -m venv venv
+source venv/bin/activate
+```
+within each research folder, install required packages for training
+```
+pip install --upgrade pip
+pip install -r requirements.txt
+```