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View Code? Open in Web Editor NEWLow-Interaction Privacy-Preserving Deep Learning via Function Secret Sharing
Low-Interaction Privacy-Preserving Deep Learning via Function Secret Sharing
Hi, what are the versions of syft, syft-proto, torch and torchvision? The latest PySyft does not support your project.
Hi! I was trying to run the WAN setting by installing from the source. I followed all instructions except two things: 1. I created a python venv for the project and 2. I put all folders into ~/work instead of in ~. I've changed the home dir in the .sh files accordingly.
Without adding the --websocket flag, the experiment runs normally. However if with it, I got the following error saying "Connection refused".
Do I have to do something in advance to initialize the port? The firewall on the server should be closed.
Hi, in the ryffel/ariaNN branch of OpenMined/PySyft ryffel/0.2.x-fix-training, we need import torchscprng, but torchscprng don't support torch==1.4.0. However, we need torch==1.4.0 for syft==0.2.9, how to resolve this problem? Thanks.
Hi! I'm attempting to test out the code with certain parameters and I've noticed that it is impossible to train the Network2 model when the --public
argument is passed. Is there a specific reason for this?
Traceback:
$ python main.py --model network2 --dataset mnist --train --public --epochs 5
Training over 5 epochs
model: network2
dataset: mnist
batch_size: 128
Traceback (most recent call last):
File "main.py", line 314, in <module>
run(args)
File "main.py", line 91, in run
train_time = train(args, model, private_train_loader, optimizer, epoch)
File "~/new_test/ariann/procedure.py", line 61, in train
loss[0] = forward(optimizer, model, data, target)
File "~/new_test/ariann/procedure.py", line 50, in forward
loss_enc = output.cross_entropy(target)
File "~/PySyft/syft/generic/frameworks/hook/hook.py", line 162, in overloaded_native_method
method = getattr(self, f"native_{method_name}")
AttributeError: 'Tensor' object has no attribute 'native_cross_entropy'
Training on other models such as LeNet or Network1, works correctly, with and without the --public argument. Training on Network2 works if the --public
argument is not used.
The problem seems to be how the CrossEntropyLoss is called with PySyft -- namely, it fails to get the correct attribute from PyTorch, if the values are unencrypted/secret shared. After attempting to change the hook.py code to use 'cross_entropy' instead of 'native_cross_entropy' it gets stuck in a loop and causes a RecursionError:
File "~/PySyft/syft/generic/frameworks/hook/hook.py", line 174, in overloaded_native_method
raise route_method_exception(e, self, args, kwargs)
File "~/PySyft/syft/generic/frameworks/hook/hook.py", line 170, in overloaded_native_method
response = method(*args, **kwargs)
RecursionError: maximum recursion depth exceeded
Thank you for any information or insight you could provide!
Hi, just to be clear that the amortized results means if say we measure the inference of a 128 batch, the final results of time and comm. are divided by 128 right?
Hi there! Brilliant work from your team!
One question I have when testing: the comm_info flag does not give the total communication rounds online, which to me is a major improvement of your method. Is there any way to count that? Thanks!
Hi, I was trying to run the code by installing from the source. but there are some errors when running the code below:
>>> cd ~
>>> git clone https://github.com/OpenMined/PySyft.git
>>> cd PySyft
>>> git checkout a73b13aa84a8a9ad0923d87ff1b6c8c2facdeaa6 ## some exceptions
Note: checking out 'a73b13aa84a8a9ad0923d87ff1b6c8c2facdeaa6'.
You are in 'detached HEAD' state. You can look around, make experimental
changes and commit them, and you can discard any commits you make in this
state without impacting any branches by performing another checkout.
If you want to create a new branch to retain commits you create, you may
do so (now or later) by using -b with the checkout command again. Example:
git checkout -b <new-branch-name>
HEAD is now at a73b13a... Remove softmax logs
>>> pip install -e . ## there is sone errors here
...
Also, I found out that there is no ryffel/ariaNN branch in OpenMined/PySyft, I think this is the crux of the problem, is that the case?
Hi,
Could you please advise which version of python you were using in this repo?
Thanks in advance!
Hi,
I am trying to get the total communication overhead when training the neural networks. If I am not wrong, the --comm_info
flag only prints out the total communication per item in the test
function, but not for the train
function, right?
Happy New Year, Ryffel~
Currently I am studying Function Secret Sharing(FSS) and interested in the implementation of FSS, so I would like to know how FSS is implemented in AriaNN.
In AriaNN, PySyft is used. And then I studied the FSS implementation in PySyft, but in some branches (I used PySyft-ryffel-0.2.x-fix-training), FSS is implemented based on the sycret. However, the sycret in AriaNN does not give a specific implementation, the details are included in Dynamic link library (e.g. native.so) and cannot be viewed, I hope you can give some more information so that I can understand how to implement FSS.
To better describe the problem, here is an example:
import torch
import torch.nn.functional as F
import syft as sy
hook = sy.TorchHook(torch)
bob = sy.VirtualWorker(hook, id="bob")
alice = sy.VirtualWorker(hook, id="alice")
crypto_provider = sy.VirtualWorker(hook, id="crypto_provider")
X = torch.rand(128, 128)
X_ = X.fix_precision(precision_fractional = 4).share(bob, alice, crypto_provider=crypto_provider, protocol="fss")
Y_ = F.relu(X_) # call relu
Y = Y_.get()
In order to calculate the relu function, class DIF
will be called, and it is defined in PySyft/syft/frameworks/torch/mpc/fss.py
:
class DIF:
"""Distributed Interval Function - used for comparison"""
@staticmethod
def keygen(n_values=1):
return dif.keygen(n_values=n_values) # dif = sycret.LeFactory(n_threads=N_CORES)
@staticmethod
def eval(b, x, k_b):
# x = x.astype(np.uint64)
original_shape = x.shape
x = x.reshape(-1)
flat_result = dif.eval(b, x, k_b) # dif = sycret.LeFactory(n_threads=N_CORES)
return flat_result.astype(np.int32).astype(np.int64).reshape(original_shape)
It can be seen that the relu
function is based on the sycret
which is located in the library of the current virtual python environment /root/.../anconda3/envs/pysyft/lib/python3.7/site -packages/sycret
. In sycret
, it can be seen from from .sycret import lib
and lib.keygen
, lib.eval
that the specific FSS function is defined in lib
, but lib
cannot be viewed.
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