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
Host LLMs in Production With On-Demand GPUs
Deploy your model, get an endpoint, pay only for compute time. No GPU provisioning or infrastructure management required.
Rate This Project
Login To Rate This Project
User Reviews
Be the first to post a review of Pfl Research!
