FLEXible (Federated Learning Experiments) is a Python framework offering tools to simulate FL with deep learning. It includes built-in datasets (MNIST, CIFAR10, Shakespeare), supports TensorFlow/PyTorch, and has extensions for adversarial attacks, anomaly detection, and decision trees.

Features

  • Comes with federated datasets like MNIST, CIFAR10, Shakespeare
  • Compatible with PyTorch and TensorFlow models
  • Extensions for adversarial testing (FLEX-Clash)
  • Supports anomaly detection (flex-anomalies)
  • Decision-tree FL tools (flex-trees)
  • Blockchain simulation support (FLEX-block)

Project Samples

FLEXible Screenshot 1

Project Activity

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Categories

Federated Learning Frameworks

License

Affero GNU Public License

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FLEXible 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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