Zeno proposed in Zeno: Distributed Stochastic Gradient Descent with Suspicion-based Fault-tolerance
This example explains how to run the Zeno fusion algorithm on CNNs implemented with Keras training
on MNIST data. Data in this example preprocessed by scaling down to range from [0, 255] to [0, 1].
No other preprocessing is performed.
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 |
| Tensorflow/keras( tf 2.1) | tf |
-
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 -f zeno -m keras -n <num_parties> -d mnist -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
STARTand 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
STARTand press enter to start accepting connections.Type
REGISTERand press enter to register the party with the aggregator. -
Finally, start training by entering
TRAINin the aggregator terminal.