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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
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