To run the scenarios locally please install the requirements
pip install -r requirements.txt
If you want to run the experiments in RPIs please follow the rpi_setup_comands.md
If you want to run the experiments in docker please run docker compose up.
To run locally you only need the following commands
mpirun -np 4 python mpi_training.py -d dataset/one_hot_encoding/
or
mpirun -np 4 python mpi_custom_training.py -d dataset/one_hot_encoding/
If you want to run the single_host setting you can run
python model.py
To run on docker you will need to connect to the master container
docker exec -it --user mpiuser dl_costs-master-1 bash
Connect one time to every worker to confirm the fingerprint of the server.
ssh worker1 and then exit
After this you can run the command
mpirun -np 4 -hostfile hostfile python mpi_training.py -d dataset
or
mpirun -np 4 -hostfile hostfile python mpi_custom_training.py -d dataset
inside the code folder.
To change the hyperparameters please confirm the available options in the mpi_training.py and mpi_custom_training.py files
The results obtained with the different hyperparameters are presented in the paper_results folder
the translation for the folder names is 10_g_50_l -> (decentralized optimization with 10 global epochs and 50 local epochs) and fed_sgd_64 -> (centralized optimization with batch size 64)
- Rafael Teixeira - rgtzths
This project is licensed under the MIT License - see the LICENSE file for details
If you use this code please site our work: Teixeira, Rafael & Antunes, Mário & Gomes, Diogo & Aguiar, Rui. (2023). The learning costs of Federated Learning in constrained scenarios. 10.1109/FiCloud58648.2023.00011.