Setting out on your adventure into Federated Learning
In a world where data privacy is paramount, OpenFL stands out as a solution for federated learning (FL). This framework enables collaborative machine learning without exposing sensitive data.
In a real-world scenario using OpenFL, data and the FL plan are shared by setting up a federated learning pipeline. Each participant, known as a collaborator or site, keeps its data locally and shares only model updates or plans with a central aggregator (or federation).
How OpenFL Works
OpenFL operates by deploying an FL plan that describes model training, evaluation, and communication steps across collaborators. Each site loads its own private dataset locally and executes the FL plan instructions on this data.
Model weight updates or gradients (not the raw data) are transmitted securely to the aggregator using OpenFL's secure communication layer. The aggregator combines updates from all sites to update the global model and sends back the aggregated model parameters to the collaborators. This cycle repeats for multiple rounds until convergence.
Customisation and Extensibility
OpenFL is designed to be flexible and extensible, supporting different machine learning model types and allowing customisation of how data and model updates are handled. The key to sharing data and the FL plan is that raw data never leaves the participant sites—instead, the FL plan enables sharing of model updates or learning instructions, ensuring data privacy while enabling collaborative model training.
Getting Started with OpenFL
To begin with OpenFL, you can follow the Keras MNIST tutorial, which serves as a starting point. To experiment with different datasets, you can replace MNIST with CIFAR in the template.
In the process, you'll learn how to:
- Deploy an FL plan (often written in Python) that describes model training, evaluation, and communication steps across collaborators.
- Load, preprocess, and set up your own datasets for training.
- Set up the number of collaborators for model training in a few lines of code.
- Check the data allocated for a specific model using provided code snippets.
- Save, evaluate, and continue the model improvement process.
Further Resources
For further help, you can refer to a white paper or explore other federated learning frameworks available. OpenFL provides tutorials for Federated Learning (FL) to help you get started.
The entire project source is available on the author's GitHub.
In essence, sharing data and the FL plan using OpenFL in real-world federated learning involves distributing the model training code to each collaborator, who runs training locally on their data and shares only encrypted model updates for aggregation, thus enabling collaborative learning without exposing sensitive data.
- Data and cloud computing technology play a crucial role in OpenFL, as it facilitates secure sharing of machine learning model updates for collaborative learning, while keeping sensitive data private within the participant sites.
- OpenFL is a technology solution that leverages data and cloud computing to enable federated learning (FL), ensuring data privacy by never transferring raw data but securely exchanging encrypted model updates between collaborators and a central aggregator.
