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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
