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. Flexe and PyFlexe are 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).
We developed FLEXE to make it possible to implement and develop vehicular FL applications within the context of CAVs. It further simplifies the process of modeling specific Machine Learning (ML) and FL applications into environments suitable for CAVs. Specifically, we developed Flexe on top of the Veins network simulator to simulate the dynamics of communication between vehicles.
PyFlexe requires the following software to be installed
- OMNeT++
- 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
In order to install the necessary packages to run Flexe, just run the following command in the root directory.
conan install . --build missing -s compiler.version=10 -s compiler.libcxx=libstdc++11
Import the project into your OMNeT++ IDE workspace by clicking File > Import > General: Existing Projects into Workspace and selecting the directory you unpacked the Flexe module to.
Flexe is an extension to the well known and widely used Veins simulation framework. See the Veins website https://veins.car2x.org/ for a tutorial, documentation, and publications.
├── simulations
│ ├── grid
│ ├── small_lust
│ └── veins
└── src
├── flexe
│ ├── application
│ ├── cache
│ ├── messages
│ └── proto
├── Makefile
└── makefrag
Instant Flexe is a virtual machine you can use to quickly try out Flexe or to run Flexe on systems you do not have administrative access to. Instant Flexe
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