Beginning Your Expedition into Decentralized Machine Learning
In the world of machine learning, data privacy is paramount. That's where Federated Learning (FL) comes into play, and OpenFL, an open-source framework by Intel, is a popular choice for implementing FL in real-world applications.
OpenFL allows multiple institutions or devices to collaboratively train a machine learning model without exposing their raw data. Here's a step-by-step guide on how OpenFL facilitates this process:
- FL Plan Preparation: The first step is to prepare and share the FL plan. This plan includes instructions on how participants should train the model on their local data. In OpenFL, this is a Python script or configuration shipped with the OpenFL task.
- Local Training: Participants run the local training tasks using the FL plan against their private datasets. These datasets are never transmitted outside the node.
- Secure Communication and Aggregation: OpenFL handles the secure communication and aggregation of model updates via its collaboration protocol.
- Model Update Combination: The aggregator combines these model updates to improve the global model and then distributes the updated model back to participants for the next training round.
With OpenFL, you can experiment with various architectures to optimise performance for specific problems. The network architecture is set up in the method, which can be modified to plug in your own architecture.
To get started, you can use the Keras MNIST tutorial as a starting point. For those who wish to experiment with another image dataset, such as CIFAR, OpenFL provides tutorials to guide you through the process.
Remember, the number of collaborators, the number of rounds for model training, and the data allocation can all be set in the appropriate methods. The data loader in OpenFL ensures that an appropriate portion of the entire dataset is allocated to each model.
After model training halts, it's essential to save the model and evaluate its performance. The entire project source is available on the author's GitHub, and you can find the code developed by Jagdish Kharatmol there.
OpenFL is just one of the popular federated learning frameworks available. By embracing federated learning, you can make significant strides in machine learning when data privacy is crucial.
In OpenFL's implementation of federated learning, data privacy is maintained by allowing multiple institutions or devices to collectively train a machine learning model without revealing their raw data, thereby leveraging the power of data-and-cloud-computing and technology. The artificial-intelligence-driven OpenFL facilitates this process via a secure communication and aggregation protocol, ensuring model updates are combined to improve the overall global model, while individual datasets remain private.
