| Name | metafed-fl JSON |
| Version |
1.0.0
JSON |
| download |
| home_page | https://github.com/afrilab/MetaFed-FL |
| Summary | Federated Learning for Metaverse Infrastructures with MARL, Privacy, and Sustainability |
| upload_time | 2025-08-28 08:53:46 |
| maintainer | None |
| docs_url | None |
| author | Muhammet Anil Yagiz, Zeynep Sude Cengiz, Polat Goktas |
| requires_python | >=3.9 |
| license | MIT License
Copyright (c) 2025 AFRI Lab
Permission is hereby granted, free of charge, to any person obtaining a copy
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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The above copyright notice and this permission notice shall be included in all
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
|
| keywords |
federated learning
machine learning
distributed systems
privacy preserving
multi-agent reinforcement learning
carbon aware computing
sustainable ai
metaverse
pytorch
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
torch
torchvision
numpy
pandas
matplotlib
timm
pyyaml
tqdm
scikit-learn
seaborn
plotly
ipywidgets
|
| Travis-CI |
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|
# MetaFed-FL: Federated Learning for Metaverse Systems
<div align="center">
<!-- Status and Quality Badges -->
<!-- Project Information Badges -->
<!-- Research and Academic Badges -->
</div>
---
## ๐ฌ Research Overview
**MetaFed** is a cutting-edge federated learning (FL) framework specifically designed for **Metaverse infrastructures**. This research addresses the critical challenges of privacy, performance, and sustainability in distributed AI systems.
**Paper:** *MetaFed: Advancing Privacy, Performance, and Sustainability in Federated Metaverse Systems*
**Authors:** Muhammet Anil Yagiz, Zeynep Sude Cengiz, Polat Goktas (2025)
๐ **[Visit Project Website](https://metafed.vercel.app)** - Interactive demos, tutorials, and detailed documentation
---
## ๐ ๏ธ Installation
### Prerequisites
- **Python 3.9+**
- **PyTorch 2.2.2+**
- **CUDA** (optional, for GPU acceleration)
### Dependencies
```bash
# Core dependencies
pip install torch>=2.2.2 torchvision>=0.17.2
pip install numpy>=1.26.4 pandas>=2.2.2
pip install matplotlib>=3.9.2 timm>=1.0.8
# Development dependencies
pip install -r requirements-dev.txt
```
### Package Installation
```bash
# Install as editable package
pip install -e .
# Or install from PyPI (when available)
pip install metafed-fl
```
---
## ๐ Quick Start
```bash
# Clone the repository
git clone https://github.com/afrilab/MetaFed-FL.git
cd MetaFed-FL
# Install dependencies
pip install -r requirements.txt
# Run MNIST experiment
python -m experiments.mnist.run_experiment
# Run CIFAR-10 experiment
python -m experiments.cifar10.run_experiment
```
---
## ๐ Key Features
๐ค **Multi-Agent Reinforcement Learning (MARL)**
- Dynamic client orchestration and selection
- Adaptive resource allocation
- Intelligent scheduling algorithms
๐ **Privacy-Preserving Techniques**
- Homomorphic encryption for secure aggregation
- Differential privacy for data protection
- Zero-knowledge proof mechanisms
๐ฑ **Carbon-Aware Scheduling**
- Real-time carbon intensity tracking
- Renewable energy-aligned orchestration
- Sustainable resource management
๐ **Comprehensive Benchmarks**
- MNIST with ResNet-18 architecture
- CIFAR-10 with ResNet-18 architecture
- Multiple federated learning algorithms
- Extensive performance metrics
---
## ๐ Performance Metrics
| Dataset | Algorithm | Accuracy Improvement | CO2 Reduction | Efficiency Gain |
|---------|-----------|---------------------|---------------|----------------|
| MNIST | FedAvg+MARL | +15% | -35% | +20% |
| CIFAR-10| FedProx+MARL| +20% | -45% | +25% |
| CIFAR-10| SCAFFOLD+MARL| +18% | -40% | +22% |
*Compared to baseline federated learning algorithms*
---
## ๐ Project Structure
```
MetaFed-FL/
โโโ src/metafed/ # Core package
โ โโโ core/ # FL core components
โ โโโ algorithms/ # FL algorithms
โ โโโ orchestration/ # MARL orchestration
โ โโโ privacy/ # Privacy mechanisms
โ โโโ green/ # Carbon-aware features
โ โโโ utils/ # Utilities
โโโ experiments/ # Experiment runners
โ โโโ mnist/ # MNIST experiments
โ โโโ cifar10/ # CIFAR-10 experiments
โโโ tests/ # Test suite
โโโ docs/ # Documentation
โโโ scripts/ # Utility scripts
```
---
## ๐งช Running Experiments
### Command Line Interface
```bash
# MNIST with FedAvg + MARL
metafed-mnist --algorithm fedavg --orchestrator rl --rounds 100
# CIFAR-10 with privacy preservation
metafed-cifar10 --algorithm fedprox --privacy differential --epsilon 1.0
# Green-aware scheduling
metafed-mnist --green-aware --carbon-tracking
```
### Configuration Files
```bash
# Using YAML configuration
python -m experiments.mnist.run_experiment --config configs/fedavg_privacy.yaml
```
### Jupyter Notebooks (Legacy)
```bash
# For research and exploration
jupyter notebook experiments/notebooks/
```
---
## ๐ง Advanced Configuration
### Algorithm Selection
- **FedAvg**: Standard federated averaging
- **FedProx**: Proximal federated optimization
- **SCAFFOLD**: Stochastic controlled averaging
### Orchestration Methods
- **Random**: Random client selection
- **RL-based**: Multi-agent reinforcement learning
- **Green-aware**: Carbon-optimized selection
### Privacy Settings
- **Homomorphic Encryption**: Secure aggregation
- **Differential Privacy**: ฮต-differential privacy
- **Hybrid**: Combined privacy mechanisms
---
## ๐ค Contributing
We welcome contributions! Please see our [Contributing Guide](CONTRIBUTING.md) for details.
### Development Setup
```bash
# Clone and setup development environment
git clone https://github.com/afrilab/MetaFed-FL.git
cd MetaFed-FL
pip install -e ".[dev]"
# Run tests
pytest tests/
# Run linting
flake8 src/ tests/
black src/ tests/
```
### Code Style
- **Black** for code formatting
- **Flake8** for linting
- **isort** for import sorting
- **Type hints** for better code documentation
---
## ๐ License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
---
## ๐ Citation
If you use MetaFed-FL in your research, please cite:
```bibtex
@misc{yagiz2025metafedadvancingprivacyperformance,
title={MetaFed: Advancing Privacy, Performance, and Sustainability in Federated Metaverse Systems},
author={Muhammet Anil Yagiz and Zeynep Sude Cengiz and Polat Goktas},
year={2025},
eprint={2508.17341},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2508.17341}
}
```
---
## ๐ Acknowledgments
- **AFRI Lab** for supporting this research
- **PyTorch Team** for the excellent deep learning framework
- **Federated Learning Community** for inspiration and collaboration
---
<div align="center">
**[๐ Website](https://metafed.vercel.app)**
*Built with โค๏ธ for the Federated Learning Community*
</div>
Raw data
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"author_email": "Muhammet Anil Yagiz <anill.yagiz@gmail.com>, Zeynep Sude Cengiz <z.sude.cengiz@gmail.com>, Polat Goktas <polatgoktas@gmail.com>",
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"description": "# MetaFed-FL: Federated Learning for Metaverse Systems\r\n\r\n<div align=\"center\">\r\n\r\n<!-- Status and Quality Badges -->\r\n\r\n\r\n\r\n\r\n\r\n<!-- Project Information Badges -->\r\n\r\n\r\n\r\n\r\n<!-- Research and Academic Badges -->\r\n\r\n\r\n\r\n\r\n</div>\r\n\r\n---\r\n\r\n## \ud83d\udd2c Research Overview\r\n\r\n**MetaFed** is a cutting-edge federated learning (FL) framework specifically designed for **Metaverse infrastructures**. This research addresses the critical challenges of privacy, performance, and sustainability in distributed AI systems.\r\n\r\n**Paper:** *MetaFed: Advancing Privacy, Performance, and Sustainability in Federated Metaverse Systems* \r\n**Authors:** Muhammet Anil Yagiz, Zeynep Sude Cengiz, Polat Goktas (2025)\r\n\r\n\ud83c\udf10 **[Visit Project Website](https://metafed.vercel.app)** - Interactive demos, tutorials, and detailed documentation\r\n\r\n---\r\n\r\n## \ud83d\udee0\ufe0f Installation\r\n\r\n### Prerequisites\r\n\r\n- **Python 3.9+**\r\n- **PyTorch 2.2.2+**\r\n- **CUDA** (optional, for GPU acceleration)\r\n\r\n### Dependencies\r\n\r\n```bash\r\n# Core dependencies\r\npip install torch>=2.2.2 torchvision>=0.17.2\r\npip install numpy>=1.26.4 pandas>=2.2.2\r\npip install matplotlib>=3.9.2 timm>=1.0.8\r\n\r\n# Development dependencies\r\npip install -r requirements-dev.txt\r\n```\r\n\r\n### Package Installation\r\n\r\n```bash\r\n# Install as editable package\r\npip install -e .\r\n\r\n# Or install from PyPI (when available)\r\npip install metafed-fl\r\n```\r\n\r\n---\r\n\r\n## \ud83d\ude80 Quick Start\r\n\r\n```bash\r\n# Clone the repository\r\ngit clone https://github.com/afrilab/MetaFed-FL.git\r\ncd MetaFed-FL\r\n\r\n# Install dependencies\r\npip install -r requirements.txt\r\n\r\n# Run MNIST experiment\r\npython -m experiments.mnist.run_experiment\r\n\r\n# Run CIFAR-10 experiment\r\npython -m experiments.cifar10.run_experiment\r\n```\r\n\r\n---\r\n\r\n## \ud83d\udcca Key Features\r\n\r\n\ud83e\udd16 **Multi-Agent Reinforcement Learning (MARL)**\r\n- Dynamic client orchestration and selection\r\n- Adaptive resource allocation\r\n- Intelligent scheduling algorithms\r\n\r\n\ud83d\udd12 **Privacy-Preserving Techniques**\r\n- Homomorphic encryption for secure aggregation\r\n- Differential privacy for data protection\r\n- Zero-knowledge proof mechanisms\r\n\r\n\ud83c\udf31 **Carbon-Aware Scheduling**\r\n- Real-time carbon intensity tracking\r\n- Renewable energy-aligned orchestration\r\n- Sustainable resource management\r\n\r\n\ud83d\udcc8 **Comprehensive Benchmarks**\r\n- MNIST with ResNet-18 architecture\r\n- CIFAR-10 with ResNet-18 architecture\r\n- Multiple federated learning algorithms\r\n- Extensive performance metrics\r\n\r\n---\r\n\r\n## \ud83d\udcc8 Performance Metrics\r\n\r\n| Dataset | Algorithm | Accuracy Improvement | CO2 Reduction | Efficiency Gain |\r\n|---------|-----------|---------------------|---------------|----------------|\r\n| MNIST | FedAvg+MARL | +15% | -35% | +20% |\r\n| CIFAR-10| FedProx+MARL| +20% | -45% | +25% |\r\n| CIFAR-10| SCAFFOLD+MARL| +18% | -40% | +22% |\r\n\r\n*Compared to baseline federated learning algorithms*\r\n\r\n---\r\n\r\n## \ud83d\udcc2 Project Structure\r\n\r\n```\r\nMetaFed-FL/\r\n\u251c\u2500\u2500 src/metafed/ # Core package\r\n\u2502 \u251c\u2500\u2500 core/ # FL core components\r\n\u2502 \u251c\u2500\u2500 algorithms/ # FL algorithms\r\n\u2502 \u251c\u2500\u2500 orchestration/ # MARL orchestration\r\n\u2502 \u251c\u2500\u2500 privacy/ # Privacy mechanisms\r\n\u2502 \u251c\u2500\u2500 green/ # Carbon-aware features\r\n\u2502 \u2514\u2500\u2500 utils/ # Utilities\r\n\u251c\u2500\u2500 experiments/ # Experiment runners\r\n\u2502 \u251c\u2500\u2500 mnist/ # MNIST experiments\r\n\u2502 \u2514\u2500\u2500 cifar10/ # CIFAR-10 experiments\r\n\u251c\u2500\u2500 tests/ # Test suite\r\n\u251c\u2500\u2500 docs/ # Documentation\r\n\u2514\u2500\u2500 scripts/ # Utility scripts\r\n```\r\n\r\n---\r\n\r\n## \ud83e\uddea Running Experiments\r\n\r\n### Command Line Interface\r\n\r\n```bash\r\n# MNIST with FedAvg + MARL\r\nmetafed-mnist --algorithm fedavg --orchestrator rl --rounds 100\r\n\r\n# CIFAR-10 with privacy preservation\r\nmetafed-cifar10 --algorithm fedprox --privacy differential --epsilon 1.0\r\n\r\n# Green-aware scheduling\r\nmetafed-mnist --green-aware --carbon-tracking\r\n```\r\n\r\n### Configuration Files\r\n\r\n```bash\r\n# Using YAML configuration\r\npython -m experiments.mnist.run_experiment --config configs/fedavg_privacy.yaml\r\n```\r\n\r\n### Jupyter Notebooks (Legacy)\r\n\r\n```bash\r\n# For research and exploration\r\njupyter notebook experiments/notebooks/\r\n```\r\n\r\n---\r\n\r\n## \ud83d\udd27 Advanced Configuration\r\n\r\n### Algorithm Selection\r\n\r\n- **FedAvg**: Standard federated averaging\r\n- **FedProx**: Proximal federated optimization\r\n- **SCAFFOLD**: Stochastic controlled averaging\r\n\r\n### Orchestration Methods\r\n\r\n- **Random**: Random client selection\r\n- **RL-based**: Multi-agent reinforcement learning\r\n- **Green-aware**: Carbon-optimized selection\r\n\r\n### Privacy Settings\r\n\r\n- **Homomorphic Encryption**: Secure aggregation\r\n- **Differential Privacy**: \u03b5-differential privacy\r\n- **Hybrid**: Combined privacy mechanisms\r\n\r\n---\r\n\r\n## \ud83e\udd1d Contributing\r\n\r\nWe welcome contributions! 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