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

See All Activity >

Categories

Federated Learning Frameworks

License

Apache License V2.0

Follow Pfl Research

Pfl Research Web Site

Other Useful Business Software
$300 in Free Credit Towards Top Cloud Services Icon
$300 in Free Credit Towards Top Cloud Services

Build VMs, containers, AI, databases, storage—all in one place.

Start your project in minutes. After credits run out, 20+ products include free monthly usage. Only pay when you're ready to scale.
Get Started
Rate This Project
Login To Rate This Project

User Reviews

Be the first to post a review of Pfl Research!

Additional Project Details

Operating Systems

Linux, Mac, Windows

Programming Language

Python

Related Categories

Python Federated Learning Frameworks

Registered

2025-07-15
Report inappropriate content