tensorflow-federated
An open-source framework for machine learning and other computations on decentralized data
GraphCanon updated 3w · GitHub synced 3w
Decision brief
TensorFlow Federated enables decentralized machine learning and computations without sharing raw data.
Good fit when
- If you need to develop federated learning algorithms that can train models across multiple devices or servers while keeping the training data distributed and secure.
- When your project involves collaborating with multiple parties who hold sensitive data and cannot share it due to privacy concerns but wish to contribute to a common model.
Avoid when
- Avoid if you require centralized data for your learning models, as TensorFlow Federated's strength lies in its capabilities to maintain decentralized datasets.
- If real-time computation or very low latency requirements are critical to your project; the nature of federated learning involves significant overhead and does not perform well in such scenarios.
Observed Jul 17, 2026 · Source: enrich:decision_facts
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Maintenance and security
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- Very active (0d since push)
- As of 3w
- Provenance
- Not a fork · Organization account
- As of 3w
- Security (OSV)
- No lockfile
- As of 1mo
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Install
pip install tensorflow-federatedHow it fits your stack(1)
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Evidence and technical details
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Overview
TensorFlow Federated is an open-source project by Google that provides tools to develop Federated Learning algorithms.
Capability facts
- Languages
- python
Source: github.language+pyproject.toml · Aug 4, 2026
Categories
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README
Installation
See the install documentation for instructions on how to install TensorFlow Federated as a package or build TensorFlow Federated from source.
Getting Started
See the get started documentation for instructions on how to use TensorFlow Federated.
For agents
This page has a .md twin and JSON over the API.