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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
Categories
Federated Learning FrameworksLicense
Affero GNU Public LicenseFollow FLEXible
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