Description:

tensorflowfederatednightly 0.19.0.dev20220218

TensorFlow Federated (TFF) is an open-source framework for machine learning andother computations on decentralized data. TFF has been developed to facilitateopen research and experimentation with Federated Learning (FL), an approach tomachine learning where a shared global model is trained across manyparticipating clients that keep their training data locally. For example, FL hasbeen used to train prediction models for mobile keyboards without uploadingsensitive typing data to servers.TFF enables developers to use the included federated learning algorithms withtheir models and data, as well as to experiment with novel algorithms. Thebuilding blocks provided by TFF can also be used to implement non-learningcomputations, 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 theycould be deployed to diverse runtime environments. Included with TFF is asingle-machine simulation runtime for experiments. Please visit thetutorials and try it out yourself!

License

For personal and professional use. You cannot resell or redistribute these repositories in their original state.

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