Fl framework assembly: PySyft

What is the PySyft Tool Assembly?

PySyft is an open-source library developed by the OpenMined community for secure and private data science. It enables data scientists to perform computations on data they cannot directly access, using privacy-enhancing technologies (PETs) such as federated learning, homomorphic encryption, and multi-party computation. PySyft is designed to facilitate structured transparency and secure data collaboration across domains, making it possible to answer research questions without exposing sensitive data.

What can you use the PySyft Tool Assembly for?

• Secure and private data analysis across various domains (healthcare, finance, research)
• Federated learning and privacy-preserving machine learning
• Homomorphically encrypted operations and split neural networks for inference
• Enabling structured transparency in data flows

Limitations/Remarks

• Steep learning curve for new users
• Complexity in deployment and configuration
• Dependent on active community engagement and support

Specific Use Cases/Applications

• Privacy-preserving medical data analysis
• Secure collaborative research across institutions
• Federated analytics for sensitive datasets
• Structured transparency systems in regulated industries

How to access the PySyft Tool Assembly?

• Official Documentation
• GitHub Repository
• Install via pip: pip install syft
• Community support via OpenMined Slack

References

• OpenMined PySyft Documentation
• PySyft GitHub
• Ziller, A. et al. (2021). PySyft: A Library for Easy Federated Learning. In: Federated Learning Systems. Springer. DOI

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Original Framework Contributors

• Andrew Trask
• OpenMined Community

For a full list, see the PySyft GitHub contributors.

More information

Training

Affiliations Contributors