TensorFlow Federated is an open-source federated learning framework.
Project description
TensorFlow Federated (TFF) is an open-source framework for machine learning and
other computations on decentralized data. TFF has been developed to facilitate
open research and experimentation with Federated Learning (FL), an approach to
machine learning where a shared global model is trained across many
participating clients that keep their training data locally. For example, FL has
been used to train prediction models for mobile keyboards without uploading
sensitive typing data to servers.
TFF enables developers to use the included federated learning algorithms with
their models and data, as well as to experiment with novel algorithms. The
building blocks provided by TFF can also be used to implement non-learning
computations, such as aggregated analytics over decentralized data.
TFF's interfaces are organized in two layers:
* Federated Learning (FL) API
The `tff.learning` layer offers a set of high-level interfaces that allow
developers to apply the included implementations of federated training and
evaluation to their existing TensorFlow models.
* Federated Core (FC) API
At the core of the system is a set of lower-level interfaces for concisely
expressing novel federated algorithms by combining TensorFlow with distributed
communication operators within a strongly-typed functional programming
environment. This layer also serves as the foundation upon which we've built
`tff.learning`.
TFF enables developers to declaratively express federated computations, so they
could be deployed to diverse runtime environments. Included with TFF is a
single-machine simulation runtime for experiments. Please visit the
tutorials and try it out yourself!
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