Download
A fast, modular Python framework released by Apple for privacy-preserving federated learning (PFL) simulation. Integrates with TensorFlow, PyTorch, and classical ML, and offers high-speed distributed simulation (7–72× faster than alternatives).
Features
- Simulates federated privacy-aware learning workflows
- Compatible with TensorFlow, PyTorch, scikit-learn
- Scales across processes, GPUs, multi-machine (via Horovod)
- Modular design for plugging privacy algorithms
- Benchmark suite for standardized comparisons
- Actively maintained by Apple researchers
Project Samples
Categories
Federated Learning FrameworksLicense
Apache License V2.0Follow Pfl Research
Other Useful Business Software
Go from Code to Production URL in Seconds
Skip the Kubernetes configs. Cloud Run handles HTTPS, scaling, and infrastructure automatically. Two million requests free per month.
Rate This Project
Login To Rate This Project
User Reviews
Be the first to post a review of Pfl Research!
