docs: new features in CML 1.6 #735
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RomanBredehoft
previously approved these changes
Jun 18, 2024
jfrery
previously approved these changes
Jun 18, 2024
andrei-stoian-zama
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yuxizama
reviewed
Jun 19, 2024
Co-authored-by: yuxizama <[email protected]>
Co-authored-by: yuxizama <[email protected]>
Co-authored-by: yuxizama <[email protected]>
Co-authored-by: yuxizama <[email protected]>
Co-authored-by: yuxizama <[email protected]>
Co-authored-by: yuxizama <[email protected]>
fd0r
reviewed
Jun 20, 2024
| @@ -12,7 +12,7 @@ Concrete ML is an open source, privacy-preserving, machine learning framework ba | |||
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| - **Training on encrypted data**: FHE is an encryption technique that allows computing directly on encrypted data, without needing to decrypt it. With FHE, you can build private-by-design applications without compromising on features. Learn more about FHE in [this introduction](https://www.zama.ai/post/tfhe-deep-dive-part-1) or join the [FHE.org](https://fhe.org) community. | |||
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| - **Federated learning**: 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. Concrete ML can import linear models, including logistic regression, that are trained using federated learning using the [`from_sklearn` function](../built-in-models/linear.md#pre-trained-models). | |||
| - **Federated learning**: 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. Concrete ML can import all types of models: linear, tree-based and neural networks, that are trained using federated learning using the [`from_sklearn_model` function](../built-in-models/linear.md#pre-trained-models) and the [`compile_torch_model`](../deep-learning/torch_support.md) function. | |||
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| - **Federated learning**: 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. Concrete ML can import all types of models: linear, tree-based and neural networks, that are trained using federated learning using the [`from_sklearn_model` function](../built-in-models/linear.md#pre-trained-models) and the [`compile_torch_model`](../deep-learning/torch_support.md) function. | |
| - **Federated learning**: 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. Concrete ML can import all types of models: linear, tree-based and neural networks using the [`from_sklearn_model` function](../built-in-models/linear.md#pre-trained-models) and the [`compile_torch_model`](../deep-learning/torch_support.md) function. |
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Not sure why we only mention federated learning here?
Or at least the sentence seems a bit restrictive 🤔
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we mention pre-trained import (not in the context of FL) on the built-in model pages
fd0r
approved these changes
Jun 20, 2024
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Add docs for new features
Closes https://github.com/zama-ai/concrete-ml-internal/issues/4432
Closes https://github.com/zama-ai/concrete-ml-internal/issues/4478