Tensorflow Federated
Federated learning framework for decentralized data
- Status
- Not yet determined
- Last checked
- Unverified
- License
- Open source
- Pricing
- Free tier · from $0
What it is
Tensorflow Federated is a federated learning framework that enables machine learning and other computations on decentralized data, promoting privacy and scalability.
Tensorflow Federated stands out as the premier framework for enabling machine learning on decentralized data, prioritizing privacy while offering flexibility for research and development.
When it fits — and when it doesn’t
A good fit for
Developers working on privacy-preserving machine learning projects
Teams needing to train models across multiple decentralized devices or clients
Researchers exploring federated learning and its applications
Look elsewhere if
Projects requiring real-time data aggregation and processing
Applications where centralized data storage is not a concern
Known weaknesses
Each one links to the public issue, changelog or docs page it came from, where we have one.
- 01
Limited integrations with other ML frameworks and tools
Primary integration is with TensorFlow, limited support for PyTorch or other popular frameworks
- 02
Performance overhead due to federated learning architecture
Communication and synchronization between clients can introduce latency and reduce overall model training speed
What it does well
Supports federated learning on decentralized data
Enables privacy-preserving machine learning
Flexible and extensible framework for research
Pricing
- Free tier
- YesOpen source — free to use
- Paid from
- $0
- Model
- Flat rate
- Enterprise plan
- No
Pricing not independently confirmed — check the vendor’s page.
Performance benchmarks
How Fast Is It?
Set up Tensorflow Federated
Setup steps, common gotchas and what to check after 30 days.