Open Federated Learning (OpenFL) is a Python library for federated learning that enables organizations to collaboratively train a model without sharing sensitive information.
In this blog post, we’ll introduce key features of OpenFL, discuss why it’s a solid choice for data scientists looking to leverage the power of distributed computing and how you can get involved.
Developed and hosted by Intel, the Linux Foundation and AI & Data Foundation Technical Advisory Council accepted it as an incubation project in March 2023.
How federated learning works
Federated learning is a machine learning method that allows data sharing across different locations or devices while keeping the data secure and private. This means that models can be trained on a user’s device without having to send their data to a centralized server.
OpenFL provides an efficient way to implement federated learning models. It supports distributed computation with low latency and is optimized for resource-constrained devices. By using OpenFL, researchers can easily build models that are secure and private while still being able to leverage the power of distributed computing.
For real-world examples, read this case study where Intel Labs collaborated with 71 international healthcare and research institutions to train AI models to identify brain tumors. The project demonstrated the ability to improve brain tumor detection by 33 percent. Check out the video below for details on the collaboration with Frontier Development Lab or FDL, a public-private partnership between NASA, the SETI Institute and Trillium Technologies.
Decentralized edge allows for data sharing across different locations or devices while keeping the data secure and private. This means that data can be stored on a user’s device without having to send it to a centralized server. OpenFL provides an efficient way to implement decentralized edge solutions. It supports distributed computation with low latency and is optimized for resource-constrained devices.
Here’s an overview of core components:
Director: The director plays a central role in OpenFL, serving as the core element. It takes charge of coordinating the federated learning process by handling communication between participants, aggregating updates to the model and evaluating the model’s performance.
Envoy: Each participant’s device runs a lightweight component called the envoy. It’s responsible for gathering local data, communicating with the director and sending updates to the model.
Frontend API: To facilitate easy interaction with OpenFL, the frontend API provides a Python library with a user-friendly interface. It simplifies the setup and management of federated learning experiments, relieving users from the burden of dealing with intricate protocol details.
Plugins: OpenFL prioritizes flexibility and extensibility, and plugins play a crucial role in this regard. They enable the integration of new functionalities into OpenFL, such as supporting various deep learning frameworks or incorporating additional security mechanisms.
Utilities: OpenFL also offers a range of utilities, including a dataset splitter and a debugging tool specifically designed for federated learning experiments.
These are just some of the core components of OpenFL. For more details, refer to the OpenFL documentation.
What OpenFL can do for you
For starters, it’s a flexible, extensible and Open Source framework that can be used to train machine learning models without sharing sensitive data. It’s also compatible with a range of machine-learning frameworks, including TensorFlow, PyTorch and scikit-learn.
Here are some specifics on what OpenFL can do:
- Train models on sensitive data without sharing it. If your data is subject to privacy regulations, such as medical data or financial data, OpenFL will keep it private.
- Build more accurate models. Federated learning can help improve the accuracy of machine learning models by taking advantage of the diversity of data held by different organizations.
- Scale up machine learning models. You can use it to train machine learning models on a much larger scale than traditional methods, which can lead to more accurate and robust models.
- Reduce the cost of training machine learning models. It can help reduce the cost of training machine learning models by eliminating the need to move data to a central location.
Ready to do more with OpenFL? Here are some resources:
- GitHub OpenFL (If you’re already an expert, try solving some issues, especially those marked “good first issue.”)
- Try out more tutorials
- Read this blog post explaining how to train a model with OpenFL
- Check out the online documentation to launch your first federation.
- Go to the virtual community meetings, here’s the calendar with time zones in multiple regions.
- Take a look at this demo