ccfraud-vetical-fedonce.md

Federated cross-geo credit card fraud detection

Scenario - This is a short example where we showcase possibilities of using Azure Machine Learning(AML) for training a model for credit card fraud detection in vertical federating learning fashion, while transferring the intermediate outputs only once. The example is inspired by work from Wu et. al. https://arxiv.org/abs/2208.10278. We have simulated a FL scenario by splitting the features for each sample into distinct geo-location. The example utilizes unsupervised learning to create latent representation of samples for each contributor. This allows for higher utilization of available data as the samples used for unsupervised training do not need to overlap across parties.

Dataset - This example is trained using the Kaggle dataset Credit Card Transactions Fraud Detection Dataset. This dataset is generated using a simulation that contains both genuine and fraudulent transactions.

Table of contents

• Federated cross-geo credit card fraud detection
  • Table of contents
  • Install the required dependencies
  • Provision an FL sandbox (with your kaggle credentials!)
  • Run a job to download and store the dataset in each silo
  • Run the demo experiment
  • Beyond the experiment

Install the required dependencies

You'll need python to submit experiments to AzureML. You can install the required dependencies by running:

conda env create --file ./examples/pipelines/environment.yml
conda activate fl_experiment_conda_env

Alternatively, you can just install the required dependencies:

python -m pip install -r ./examples/pipelines/requirements.txt

Provision an FL sandbox (with your kaggle credentials!)

To run this example, you will need to provision one of our sandboxes. Any sandbox should work with this tutorial below (if not, please reach out). We will use the same names for the computes and datastores created by default in those sandboxes.

If you have already provisioned a sandbox during the quickstart you can reuse it, but you need to make sure you have added your Kaggle credentials so we can upload the required dataset in each silo. If not, please refer to our tutorial on how to add your kaggle credentials to the workspace secret store before running the following sections.

You can also re-provision another sandbox with a different base name using the deploy buttons on the sandbox page, and provide your kaggleUsername and kaggleKey as parameters, so they will be injected securely in your workspace secret store.

📓 take note of your workspace name, resource group and subscription id. You will need them to submit the experiment.

Run a job to download and store the dataset in each silo

This can all be performed with ease using a data provisioning pipeline. To run it follow these steps:

1. If you are not using the sandbox default setup, adjust the config file config.yaml in examples/pipelines/utils/upload_data/ to match your setup.

2. Submit the experiment by running:

   python ./examples/pipelines/utils/upload_data/submit.py --example CCFRAUD_VERTICAL_FEDONCE --workspace_name "<workspace-name>" --resource_group "<resource-group-name>" --subscription_id "<subscription-id>"

   Note: You can use --offline flag when running the job to just build and validate pipeline without submitting it.

   ⭐ you can simplify this command by entering your workspace details in the file config.yaml in this same directory.

⚠️ Proceed to the next step only once the pipeline completes. This pipeline will create data in 3 distinct locations.

Run the demo experiment

1. If you are not using the sandbox default setup, adjust the config file config.yaml in examples/pipelines/ccfraud_vertical_fedonce/ to match your setup.

2. Submit the FL experiment by running:

   python ./examples/pipelines/ccfraud_vertical_fedonce/submit.py --workspace_name "<workspace-name>" --resource_group "<resource-group-name>" --subscription_id "<subscription-id>"

   Note: You can use --offline flag when running the job to just build and validate pipeline without submitting it.

   ⭐ you can simplify this command by entering your workspace details in the file config.yaml in this same directory.

Beyond the experiment

The example above describe split-learning vertical federated learning scenario. However, we also provide an example for Vertical Federated Learning with Split Learning.