fedavg

Running Federated Averaging with Keras

FedAvg proposed in: Communication-Efficient Learning of Deep Networks from Decentralized Data

This example explains how to run the federated averaging algorithm 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, Scikit Learn, keras(tf 2.1) can be creating by changing -m param.

Model Type	Params
Keras (with tf 1.15)	keras
Pytorch	pytorch
Scikit learn	sklearn
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 fedavg -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.