Today, a comprehensive demonstration video was released, showcasing the capabilities of the Federated Learning Flower Framework-based tool. The video guides viewers through three key stages of the interactive process, highlighting its user-friendly interface and advanced machine learning features.
In the initial stage, users are introduced to the graphical user interface, enabling them to select tasks, datasets, models, and federated learning algorithms with ease. For example, users can choose prediction tasks, datasets like MNIST or Fashion MNIST, and specify whether to train new models or utilize pre-trained ones. The tool supports the selection of federated learning algorithms such as federated averaging and personalized federated learning with Moreau envelopes.
The second stage demonstrates the training process, which runs in the background for several rounds. The video shows real-time metrics like loss and accuracy, with accuracy reaching up to 94%, underscoring the tool’s’s effectiveness. Users can view detailed results through intuitive graphs displaying training progress and modelmetrics over training rounds.
In the final stage, the demonstration highlights how users can analyze results post-training. Graphs display the evolution of loss and accuracy, and in more advanced scenarios, compare centralized models with personalized federated models, revealing significant performance improvements.
The video also explores a variant where users select a heterogeneous Fashion MNIST dataset, split so each client only has five classes. This setup demonstrates the framework’s flexibility and effectiveness in scenarios with non-i.i.d. data, showcasing the benefits of personalized federated learning algorithms.
Overall, the demonstration underscores theFlower-based FL tool’s ease of use, versatility, and powerful capabilities for federated learning applications. For more information or to try the tool yourself, visit the official resources.
Watch demo on our YouTube channel or here below: