Tensorflow Federated
An open-source framework for machine learning and other computations on decentralized data.
What is Tensorflow Federated?
TensorFlow Federated is an open-source framework for machine learning and computations on decentralized data. It enables Federated Learning, where a shared global model is trained across many participating clients without centralizing their sensitive data, solving privacy and data governance challenges in distributed environments.
Machine learning researchers and developers working on privacy-preserving AI, distributed systems, or novel federated learning algorithms who need a flexible framework for experimentation and deployment.
Developers choose TFF for its layered APIs that provide both high-level ease of use for applying federated learning to existing models and low-level flexibility for inventing new algorithms, all backed by TensorFlow integration and a simulation runtime for rapid experimentation.
Overview
An open-source framework for machine learning and other computations on decentralized data.
Use Cases
Best For
- Training machine learning models on sensitive data without centralizing it (e.g., mobile keyboard predictions)
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