Code for the paper: Comparative Evaluation of Clustered Federated Learning Method

Submited to 'The 2nd IEEE International Conference on Federated Learning Technologies and Applications (FLTA24), VALENCIA, SPAIN'

1. To reproduce the results in the paper run driver.py with the parameters in exp_configs.csv

2. Each experiment will output a .csv file with the resuting metrics

3. Histogram plots and a summary table of various experiments can be obtained running src/utils_results.py

To use driver.py use the following parameters :

python driver.py --exp_type --dataset --nnmodel --heterogeneity_type --num_clients --num_samples_by_label --num_clusters --centralized_epochs --federated_rounds --seed

To run all experiments in exp_config.csv user run_exp.py.

Once all experiments are done, to get results run src/utils_results.src.

Paper results were done on dataset Mnist, Fashion-Mnist and Kmnist.

Added option to use cifar-10 and convolutional model which is not in the original paper.
Use parameters --dataset cifar10 --nnmodel convolutional.