Fl framework assembly: TensorFlow Federated

What is the TensorFlow Federated Tool Assembly?

TensorFlow Federated (TFF) is an open-source framework developed by Google for machine learning and analytics on decentralized data. TFF provides a two-layer architecture for building custom federated algorithms and supports both federated learning and federated analytics. It is designed for research and experimentation, enabling privacy-preserving model development without centralizing data.

What can you use the TensorFlow Federated Tool Assembly for?

• Developing custom federated learning algorithms
• Federated analytics and non-learning computations
• Privacy-preserving model training (e.g., mobile keyboard prediction)
• Research and experimentation in federated optimization

Limitations/Remarks

• Requires familiarity with TensorFlow for optimal use
• Design choices may limit flexibility with non-TensorFlow technologies

Specific Use Cases/Applications

• Mobile device model training (e.g., keyboard prediction)
• Federated analytics on decentralized data
• Research in privacy-preserving machine learning

How to access the TensorFlow Federated Tool Assembly?

• Official Documentation
• GitHub Repository
• Install via pip: pip install tensorflow-federated

References

• TensorFlow Federated Documentation
• TensorFlow Federated GitHub
• Federated Core Guide

---

Original Framework Contributors

• Google Research
• TensorFlow Federated Community

For a full list, see the TensorFlow Federated GitHub contributors.

More information

Training

Affiliations Contributors