Skip to content

Latest commit

 

History

History

gradient_aggregation

Folders and files

NameName
Last commit message
Last commit date

parent directory

..
 
 
 
 
 
 
 
 
 
 

Running Gradient average with Keras CNN in IBM federated learning

This example explains how to run federated learning on CNNs implemented with Keras training on MNIST data. Data in this example is preprocessed by scaling down to range from [0, 255] to [0, 1]. No other preprocessing is performed.

Model Setup

This experiment can be run using models with different underlying framework. By default, configs with keras(tf 1.15) based model are generated, but other models like PYTORCH, keras(tf 2.1) can be creating by changing -m param.

Model Type Params
Keras (with tf 1.15) keras
Pytorch pytorch
Tensorflow/keras( tf 2.1) tf

Setup

  • Split data by running:

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

    python examples/generate_configs.py -n <num_parties> -f gradient_aggregation -m keras -d <dataset> -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.