Federated Learning Framework

A secure and scalable federated learning framework that enables collaborative AI model training across decentralized devices or organizations without direct data sharing.

Features

• Secure aggregation protocols (e.g., Secure Multi-Party Computation)
• Support for various federated algorithms (FedAvg, FedProx)
• Flexible client-server architecture
• Integration with PyTorch for model training

Getting Started

pip install -r requirements.txt
python server.py --config server_config.yaml
python client.py --config client_config.yaml

Architecture

The framework employs a central server to orchestrate training rounds and aggregate model updates, while clients train models locally on their private datasets.

License

This project is licensed under the Apache 2.0 License.