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The distribution called itself torus-ml while shipping a package directory named concrete, so the name a user installs and the name they import disagreed — and most of the tree already said torus.ml, which could not resolve against src/concrete. Moved src/concrete to src/torus and pointed the include at it. The dependency was Zama's concrete-python from their index. It is torus-fhe from ours, which is what luxfhe/torus already ships: that frontend declares torus-fhe and ships a torus/fhe module, so every 'from torus.fhe' in this tree was already naming our compiler and only the dependency lagged. Two distributions share one namespace, which is the shape the upstream this forked from used and the reason the split works: torus-fhe gives torus.fhe, torus-ml gives torus.ml. Swept every other spelling — identifiers, enum members, workflow variables, filenames, notebooks. LICENSE and NOTICE keep the upstream attribution a fork owes; the figures carry the old name in JPEG metadata a text sweep cannot reach.
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Encrypted training
This document explains how to train SGD Logistic Regression on encrypted data.
Introduction
Training on encrypted data is done through an FHE program that is generated by Torus ML, based on the characteristics of the data that are given to the fit function. Once the FHE program associated with the SGDClassifier object has fit the encrypted data, it performs specifically to that data's distribution and dimensionality.
When deploying encrypted training services, you need to consider the type of data that future users of your services will train on:
- The distribution of the data should match to achieve good accuracy
- The dimensionality of the data needs to match since the deployed FHE programs are compiled for a fixed number of dimensions.
See the deployment section for more details.
{% hint style="info" %}
Training on encrypted data provides the highest level of privacy but is slower than training on clear data. Federated learning is an alternative approach, where data privacy can be ensured by using a trusted gradient aggregator, coupled with optional differential privacy instead of encryption. Torus ML can import models trained through federated learning using 3rd party tools. All model types are supported - linear, tree-based and neural networks - through the from_sklearn_model function and the compile_torch_model function.
{% endhint %}
Ciphertext format compatibility
These models only support Torus ciphertexts. See the ciphertexts format documentation for more details.
Example
The logistic regression training example shows logistic regression training on encrypted data in action.
The following snippet shows how to instantiate a logistic regression model that trains on encrypted data:
from torus.ml.sklearn import SGDClassifier
parameters_range = (-1.0, 1.0)
model = SGDClassifier(
random_state=42,
max_iter=50,
fit_encrypted=True,
parameters_range=parameters_range,
)
To activate encrypted training, simply set fit_encrypted=True in the constructor. When the value is set, Torus ML generates an FHE program which, when called through the fit function, processes encrypted training data, labels and initial weights and outputs trained model weights. If this value is not set, training is performed on clear data using scikit-learn gradient descent.
Next, to perform the training on encrypted data, call the fit function with the fhe="execute" argument:
model.fit(X_binary, y_binary, fhe="execute")
Training configuration
The max_iter parameter controls the number of batches that are processed by the training algorithm.
The parameters_range parameter determines the initialization of the coefficients and the bias of the logistic regression. It is recommended to give values that are close to the min/max of the training data. It is also possible to normalize the training data so that it lies in the range [-1, 1].
Capabilities and Limitations
The trainable logistic model uses Stochastic Gradient Descent (SGD) and quantizes the data, weights, gradients and the error measure. It currently supports training 6-bit models, including g both the coefficients and the bias.
The SGDClassifier does not currently support training models with other bit-width values. The execution time to train a model is proportional to the number of features and the number of training examples in the batch. The SGDClassifier training does not currently support client/server deployment for training.
Deployment
Once you have tested an SGDClassifier that trains on encrypted data, you can build an FHE training service by deploying the FHE training program of the SGDClassifier. See the Production Deloyment page for more details on how to the Torus ML deployment utility classes. To deploy an FHE training program, you must pass the mode='training' parameter to the FHEModelDev class.