🧠 [MobiCom FLEdge-AI 2025] FedNeVe: Efficient Federated Model Aggregation through Neural Velocity

This repository contains the official implementation of the paper:

Efficient Federated Model Aggregation through Neural Velocity
Gianluca Dalmasso, et al.
MobiCom FLEDGE-AI Workshop 2025
📄 [arXiv link]

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🚀 Getting Started

You can run this project either using a Python virtual environment or a Docker container.

✅ Clone the repository

git clone https://github.com/EIDOSLAB/FederatedNeVe.git
cd FederatedNeVe

🧪 Option A — Run with virtual environment (recommended for development)

📦 Create virtual environment & install dependencies

This project was developed and tested with Python 3.11 — we recommend using the same version for full compatibility and reproducibility.

# 1. Install Python 3.11 (only once)
pyenv install 3.11

# 2. Create virtual environment
pyenv virtualenv 3.11 fedneve

# 3. Activate the environment
pyenv activate fedneve

# 4. Install dependencies
pip install -r requirements.txt

# 5. From inside FederatedNeve folder install the setup.py
pip install .

🚀 Run training

cd src
python classical.py
python federated.py

🐳 Option B — Run with Docker

You can also use Docker for full environment reproducibility.

🏗️ Build Docker images and push to remote registry

The build.sh script automates the build of all Docker images and pushes them to the configured remote Docker registry.

Before running, make sure to edit build.sh to set your remote registry URL and credentials if needed.

Run:

bash build.sh

This will build the following Docker images:

• fedneve:base (default container for training and experiments)
• fedneve:python (base Python environment)
• fedneve:sweep (for hyperparameter sweep experiments)

🚀 Run training inside the container

docker run --rm -it \
  --gpus all \                   # Optional: remove if no GPU
  fedneve:python federated.py  # Optional: Optional parameters...

💡 Note: you may need to adjust volume mounting (-v) depending on your OS and Docker setup.

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📊 Datasets

Tested datasets:

• CIFAR10, and CIFAR100
• EuroSAT
• Caltech-256
• MNIST, and FashionMNIST
• Imagenette (must be downloaded separately and prepared in the standard folder format.)

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🪪 License

This project is licensed under the GNU General Public License v3.0.
See the LICENSE file for details.

➡️ You are free to use, modify, and distribute this code under the same license terms.
Any derivative work must also be distributed under the GNU GPL.

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🙌 Acknowledgments

This research was developed at the University of Turin (UniTO), within the EIDOS Lab, and University of Surrey within the NICE group.

We thank the members of both institutions for the insightful discussions and support during the development of this work.

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📜 Citation

If you use this repository or find our work helpful, please cite: (Citation will be updated upon publication)

@misc{dalmasso2025fedneve,
  title        = {Efficient Federated Model Aggregation through Neural Velocity},
  author       = {Dalmasso, Gianluca and Gusmão, Pedro Porto Buarque and Fiandrotti, Attilio and Grangetto, Marco},
  year         = {2025},
  howpublished = {https://arxiv.org/abs/xxxx.xxxxx},
  note         = {MobiCom FLEDGE-AI Workshop 2025. Official citation will be updated upon publication.}
}

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📫 Contact

For questions or collaborations, feel free to reach out:

• 📧 [email protected] / [email protected]
• 🐙 GitHub Issues for bugs or feature requests