Comments (2)
zicofish
commented on April 27, 2024
@Qi-Pang This is mainly related to truncation. When using interactive truncation, it requires to compute inverse, which is possible with base 3 but impossible with base 2. You might have to read the code and paper for the interactive truncation to understand this further. With that said, normally, we would NOT use interactive truncation, but instead we use non-interactive truncation with fixed64_ni.
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Qi-Pang
commented on April 27, 2024
I see. Thanks a lot for the info!
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Related Issues (20)
- Save and Load ABY3 Model
- Can this framework use encrypted training data to train network models? Are there any relevant cases to learn from? HOT 7
- version 0.8 federation-learning example seems contain an error in validation dataset setting HOT 2
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- 'NoneType' object has no attribute 'secure_seed' HOT 9
- TypeError: can only concatenate list (not "TensorShapeV1") to list. (An error in sample_seeded_uniform) HOT 2
- In the federated learning of examples, how to protect the DataOwner's gradient? HOT 1
- I can't install TF-Encrypted HOT 1
- Is it possible to evaluate the BaseModel with other keras metrics aside from binary and categorical accuracy? HOT 5
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- ValueError: Invalid dtype tf.int16 HOT 2
- why I can see all secret shares in one party? HOT 3
- Can't import tf_encrypted in Colab notebook
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- make: *** [Makefile:322：tf_encrypted/operations/secure_random/secure_random_module_tf_2.13.0.so]; make build error HOT 1
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