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A user of PySyft will want to use TF in the way they would normally with PyTorch. Part of that means enabling federated learning as a use case. While we do not need to support yet all of the luxuries of the PyTorch side, we do want to demonstrate that the same use cases are solvable with TensorFlow.
This issue will be complete once a basic tutorial for federated learning has been implemented and completed. This tutorial can be updated in a later issue/PR as Syft TF becomes more feature-complete (e.g. GradientTape has been implemented, etc.).
Objectives/Key Results
We have the demo code in a jupyter notebook
We're training a federated model for multiple epochs
Show loss decreasing
Use PySyft sandbox for the demo
The text was updated successfully, but these errors were encountered:
Description
A user of PySyft will want to use TF in the way they would normally with PyTorch. Part of that means enabling federated learning as a use case. While we do not need to support yet all of the luxuries of the PyTorch side, we do want to demonstrate that the same use cases are solvable with TensorFlow.
This issue will be complete once a basic tutorial for federated learning has been implemented and completed. This tutorial can be updated in a later issue/PR as Syft TF becomes more feature-complete (e.g. GradientTape has been implemented, etc.).
Objectives/Key Results
The text was updated successfully, but these errors were encountered: