To run the notebooks in each example, please make sure you first set up a virtual environment and install "./requirements.txt" and JupyterLab following the example root readme.
- cifar10 - Multi-class classification with image data using CIFAR10 dataset
- higgs - Binary classification with tabular data using HIGGS dataset
These step-by-step example series are aimed to help users quickly get started and learn about FLARE. For consistency, each example in the series uses the same dataset- CIFAR10 for image data and the HIGGS dataset for tabular data. The examples will build upon previous ones to showcase different features, workflows, or APIs, allowing users to gain a comprehensive understanding of FLARE functionalities (Note: each example is self-contained, so going through them in order is not required, but recommended). See the README in each directory for more details about each series.
Here are some common questions we aim to cover in these examples series when formulating a federated learning problem:
- What does the data look like?
- How do we compare global statistics with the site's local data statistics?
- How to formulate the federated algorithms?
- How do we convert the existing machine learning or deep learning code to federated learning code? ML to FL examples
- How do we use different types of federated learning workflows (e.g. Scatter and Gather, Cyclic Weight Transfer, Swarming learning, Vertical learning) and what do we need to change?
- How can we capture the experiment log, so all sites' metrics and global metrics can be viewed in experiment tracking tools such as Weights & Biases MLfLow, or Tensorboard