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

Pfl Research Screenshot 1

Project Activity

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Categories

Federated Learning Frameworks

License

Apache License V2.0

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Pfl Research Web Site

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Additional Project Details

Operating Systems

Linux, Mac, Windows

Programming Language

Python

Related Categories

Python Federated Learning Frameworks

Registered

2025-07-15
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