Download
APPFL (Advanced Privacy-Preserving Federated Learning) is a Python framework enabling researchers to easily build and benchmark privacy-aware federated learning solutions. It supports flexible algorithm development, differential privacy, secure communications, and runs efficiently on HPC and multi-GPU setups.
Features
- Implements differential privacy and client authentication
- Modular plug-and-play aggregation, scheduling, trainers
- Supports synchronous and asynchronous FL algorithms
- Multi-GPU training via PyTorch DDP
- Integrates with MONAI for healthcare workflows
- Scalable on HPC using MPI/gRPC-based client-server setup
Project Samples
Categories
Federated Learning FrameworksLicense
MIT LicenseFollow Appfl
Other Useful Business Software
MongoDB Atlas runs apps anywhere
MongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
Rate This Project
Login To Rate This Project
User Reviews
Be the first to post a review of Appfl!
