google-parfait/tensorflow-federated

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2448 stars · 606 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

TensorFlow Federated (TFF) is an open-source Python framework for machine learning and other computations on decentralized data. It provides high-level federated learning APIs built on TensorFlow plus a lower-level Federated Core for expressing novel federated algorithms, with a single-machine simulation runtime.

Use cases

• train a model across many clients without collecting their data
• simulate federated averaging experiments on a single machine
• apply federated training and evaluation to existing TensorFlow models
• prototype novel federated learning algorithms
• compute aggregated analytics over decentralized datasets
• train mobile keyboard prediction models without uploading sensitive user data

When to choose

• you need privacy-preserving training where data stays on client devices
• you are researching or prototyping federated learning algorithms
• your models are already built in TensorFlow
• you want a simulation runtime for federated experiments without real device infrastructure

When to avoid

• you need production federated learning on real mobile devices rather than simulation
• your stack is PyTorch or another non-TensorFlow framework
• you need simple centralized training with no decentralization constraints
• you need lightweight analytics without learning a strongly-typed federated computation model

Facets

framework · maturity active

machine-learning llm-training simulation sdk machine-learning artificial-intelligence privacy microservices python cross-platform federated-learning tensorflow decentralized-data privacy-preserving simulation-runtime

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markdown · JSON · MCP: product_card(name="google-parfait/tensorflow-federated")