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Running SPAHM for unsupervised clustering problem with KMeans

SPAHM proposed here: Statistical Model Aggregation via Parameter Matching

This example explains how to run the SPAHM algorithm on a simulated federated unsupervised clustering dataset using a KMeans model. For more details on the dataset generation process and detailed experimental results of SPAHM on this problem, please refer to Section 5 in the SPAHM paper for details.

  • Split data by running:

    python examples/generate_data.py -n <num_parties> -d federated-clustering -pp <points_per_party>
    
  • Generate config files by running:

    python examples/generate_configs.py -n <num_parties> -f spahm -m sklearn -d federated-clustering -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.