Flexe is a new framework for simulation of Federated Learning (FL) in Connected and Autonomous Vehicle (CAVs). Its adaptable design allows for the implementation of a variety of FL schemes, including horizontal, vertical, and Federated Transfer Learning. PyFlexe is free to download and use, built for customization, and allows for the realistic simulation of wireless networking and vehicle dynamics. The project was partitioned into two, one of which is responsible for vehicle-to-vehicle communication (Flexe) and the other for the construction and training of models (PyFlexe).
These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.
PyFlexe requires the following software to be installed
- OMNeT++ 6
- conan
- grpc
- TensorFlow
- PyTorch
Please do follow the instructions from the official OMNeT documentation
Please do follow the instructions from the official conan documentation
Please do follow the instructions from the official GRPC documentation
Please do follow the instructions from the official TensorFlow documentation
Please do follow the instructions from the official PyTorch documentation
A step by step series of examples that tell you how to get a development env running
Say what the step will be
Give the example
And repeat
until finished
End with an example of getting some data out of the system or using it for a little demo
Explain how to run the automated tests for this system
Explain what these tests test and why
Give an example
Explain what these tests test and why
Give an example
Each strategy is evaluated through simulation.py which creates the set of clients and the server of the selected strategy under the given setting.
Additionally, FedLTA can generate various visualizations to help researchers to analyze the results (see folder analysis).
├── analysis # Tables and plots creation - Folder
├── client # Client-side code - Folder
│ ├── client_tf # Client-side code for TensorFlow - Folder
│ ├── client_torch # Client-side code for Pytorch - Folder
├── data # Datasets - Folder
├── dataset_utils_torch.py # Dataset utilities for Pytorch
├── exc_all.sh # Executes all the experiments
├── exc_joint_plot.sh # Generates joint plots
├── exec_simulation.py # Executes the selected experiment
├── execution_log # Contains the terminal logs of each executed experiment - Folder
├── log_exc_all.txt
├── logs # Contains the results of each executed experiment - Folder
├── model_definition_tf.py # Deep learning models in TensorFlow
├── model_definition_torch.py # Deep learning models in Pytorch
├── push_logs.sh # Pushes the generated logs to GitHub
├── server # Server-side code
│ ├── common_base_server # Common base server code (i.e., it is independent of Tensorflow and Pytorch) - Folder
│ ├── server_tf # TensorFlow-specific implementations of servers - Folder
│ └── server_torch # Pytorch-specific implementations of servers - Folder
├── simulation.py # Main file. Executes the selected strategy in the Federated |Learning under the specified setting
Please read CONTRIBUTING.md for details on our code of conduct, and the process for submitting pull requests to us.
We use SemVer for versioning.
- Wellington Lobato - WellingtonLobato
- Joahannes B. D. da Costa - joahannes
- Allan M. de Souza - AllanMSouza
- Denis Rosario
- Christoph Sommer - sommer
- Leandro A. Villas
PyFlexe and Flexe can reproduce results in the following papers:
@INPROCEEDINGS{Lobato2022,
author={Lobato, Wellington and Costa, Joahannes B. D. Da and Souza, Allan M. de and Rosário, Denis and Sommer, Christoph and Villas, Leandro A.},
booktitle={2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall)},
title={FLEXE: Investigating Federated Learning in Connected Autonomous Vehicle Simulations},
year={2022},
pages={1-5},
doi={10.1109/VTC2022-Fall57202.2022.10012905}
}This project is licensed under the GPL-2.0 license - see the COPYING.md file for details