CFL

Please cite: Taghia, Jalil, Farnaz Moradi, Hannes Larsson, Xi Lan, Adam Orucu, Masoumeh Ebrahimi and Andreas Johnsson. “Congruent Learning for Self-Regulated Federated Learning in 6G.” IEEE Transactions on Machine Learning in Communications and Networking 2 (2024): 129-149.

Install CFL (Python 3.10.13):

cd congruent-federated-learning

pip install -e .

Input data

Save your data per client as a .pickle file

Example:

client 0: ./path_to_your_data/0.pickle

client 1: ./path_to_your_data/1.pickle

Provide the training data as a nested dict as: data = {'dataset': {'X': np.ndarray, 'Y': np.ndarray}, 'id': str}

dataset itself is a dict with keys 'X' and 'Y'

'id' is a string corresponding to the name of the client

A template for use of CFL in FMNIST classification:

There is a dataclass named ExpConfig which needs to be modified

There is a dataclass named LearningConfig which contains the learning configs

Notes:

CFL generally prefers to have number of epochs per round set to a larger value

You can make the final predictions either from the global model or the local model at the clinet side.

This is done by setting ExpConfig.make_predictions_using_local_model=True

To run the script:

python -m cfl.exp_scripts.exp_fmnist

Note: Data from FMNIST is used for this demo experiment as stored in ./cfl/data/fg_traces/