Dataset Setup

Currently, for Federated Prejudice Removal we support following datasets:

• Adult
• Compas

Running Federated Prejudice Removal in FL

This example provides the implementation for Federated Prejudice Removal presented in Mitigating Bias in Federated Learning. This example requires the gensim library. Run the following: pip install gensim

The following preprocessing was performed in AdultPRDataHandler on the original dataset:

• Drop following features: workclass, fnlwgt, education, marital-status, occupation, relationship, capital-gain, capital-loss, hours-per-week
• Map race, sex and class values to 0/1
• Split age and education columns into multiple columns based on value
• Map native-country values to an integer 1-7 based on continent

Further details in documentation of preprocess() in AdultPRDataHandler.

The following preprocessing was performed in CompasPRDataHandler on the original dataset:

• Map sex values to 0/1 based on underprivileged/privileged groups
• Filter out rows with values outside of specific ranges for days_b_screening_arrest, is_recid, c_charge_degree, score_text, race
• Quantify length_of_stay from c_jail_out and c_jail_in
• Quanitfy priors-count, length_age_cat, score_text, age_cat
• Drop following features: id, name, first, last, compas_screening_date, dob, age, juv_fel_count, decile_score, juv_misd_count, juv_other_count, priors_count, days_b_screening_arrest, c_jail_in, c_jail_out, c_case_number, c_offense_date, c_arrest_date, c_days_from_compas, c_charge_desc, is_recid, r_case_number, r_charge_degree, r_days_from_arrest, r_offense_date, r_charge_desc, r_jail_in, r_jail_out, violent_recid, is_violent_recid, vr_case_number, vr_charge_degree, vr_offense_date, vr_charge_desc, type_of_assessment, decile_score.1, screening_date, v_type_of_assessment, v_decile_score, v_score_text, v_screening_date, in_custody, out_custody, priors_count.1, start, end, event
• Split age-cat, priors_count and c_charge_degree columns into multiple columns based on value

Further details in documentation of preprocess() in CompasPRDataHandler.

No other preprocessing is performed.

• Set FL environment by running:

  ./setup_environment.sh <env_name>  # setup environment
  

• Activate a new FL environment by running:

  conda activate <env_name>          # activate environment
  

• Split data by running:

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

• Generate config files by running:

  python examples/generate_configs.py -n <num_parties> -f prej_remover -d <dataset_name> -p <path>
  

• In a terminal running an activated FL 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 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.