naive_bayes_dp

Running differential private simple average with Sklearn Naive Bayes models

This example explains how to run the simple averaging fusion algorithm to train a Naive Nayes model implemented with Scikit-learn on Adult Dataset data. The following preprocessing was performed in AdultSklearnDataHandler on the original dataset:

• Drop following features: workclass, fnlwgt, education, marital-status, occupation, relationship, capital-gain, capital-loss, hours-per-week, native-country
• Map race, sex and class values to 0/1
• Split age and education columns into multiple columns based on value

No other preprocessing is performed.

• Split data by running:

  python examples/generate_data.py -n <num_parties> -d adult -pp <points_per_party>
  

• Generate config files by running:

  python examples/generate_configs.py -n <num_parties> -f naive_bayes_dp -m sklearn -d adult -p <path>
  

• In a terminal running an activated IBM FL environment (refer to Quickstart in our website to learn more about how to set up the running environment), start the aggregator by running:

  python -m ibmfl.aggregator.aggregator <agg_config>
  

  Type START and press enter to start accepting connections

• In a terminal running an activated IBM FL environment, start each party by running:

  python -m ibmfl.party.party <party_config>
  

  Type START and press enter to start accepting connections.

  Type REGISTER and press enter to register the party with the aggregator.

• Finally, start training by entering TRAIN in the aggregator terminal.