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### đź“ť Description
**Functional Encryption** is a technique which relates to Homomorphic Encryption since it performs computations over encrypted data, but it also provides automatic decryption of t…
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### Brief Summary:
Describe the what, why, and how of your content idea in 2-5 sentences.
Continuation of Issue #407
Federated learning (also known as collaborative learning) is a machine learnin…
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## Description
After I update my syft to 0.2.9,I'm not allowed to import syft
Here is the traceback:
```
ModuleNotFoundError Traceback (most recent call last)
in
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Hello, `ml_privacy_meter` looks good. it was well encapsulated.
I'm going to apply your tool to evaluate my model.
And I have some questions as follow.
1. I have programmed the model with pytorch, …
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Thank you for publishing this nice work. It reports an error when I run the code at the function 'train()' in training.py, line 663. The following error displayed:
Traceback (most recent call last…
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Hi!
I have a quick question: I've seen that you have built backward hooks for many nn.Module classes where you basically compute the per-sample gradient. Do you think it could be possible to do this …
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BGV (Brakerski-Gentry-Vaikuntanathan) Homomorphic Encryption is one of the leading approaches to homomorphic encryption. While current work is underway within our community to wrap the SEAL homomorphi…
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#### Mentor: Adam J Hall
Federated Learning and SpitNN are both methods for distributing the training of a neural network across multiple machines for the sake of privacy. However, they have differ…
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Hi!
Supporting NumPy as a framework would allow it to be used for Machine Learning using Federated Learning.
As reported by @iamtrask, NumPy is used normally used in ML toolchains, so PySyft us…
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## Description
Cifar10 accuracy with resnet18 on a single client is stuck at 75%
I ran for 200 epochs with a learning rate schedule of (0.1 till epoch100, 0.01 from 100-150, 0.001 from 150-200)
The…