Coordinate Median Plus is one variation of Fed+ fusion algorithms proposed here: Fed+: A Unified Approach to Robust Personalized Federated Learning
More variations of Fed+ can be at:
This example explains how to run coordinate median plus algorithm on CNNs implemented with TensorFlow 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.
-
Split data by running:
python examples/generate_data.py -n <num_parties> -d mnist -pp <points_per_party>
For example, to generate sample data on MNIST dataset, you could run:
python examples/generate_data.py -n 2 -d mnist -pp 200
Run python examples/generate_data.py -h
for full descriptions
of the different options.
- Generate config files by running:
python examples/generate_configs.py -n <num_parties> -f coordinate_median_plus -m tf -d <dataset> -p <path>
To run FL, you must have configuration files for the aggregator and for each party.
You can generate these config files using the generate_configs.py
script.
For example, you could run:
python examples/generate_configs.py -f coordinate_median_plus -m tf -n 2 -d mnist -p examples/data/mnist/random
This command would generate the configs for the tf_classifier_mnist
model, assuming 2 parties.
You must also specify the party data path.
Run python examples/generate_configs.py -h
for full descriptions of the different options.
-
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.