> For the complete documentation index, see [llms.txt](https://docs.rhinofcp.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.rhinofcp.com/security-and-data-protections/fully-homomorphic-encryption.md).

# Fully Homomorphic Encryption

The Rhino Federated Computing platform supports **Fully Homomorphic Encryption** (FHE) via the NVFlare framework (and the TenSEAL package). 

To enable FHE in your model training, you will need to perform **two actions**:

1. Install NVFlare with FHE support in your container image, by installing `nvflare[HE]` rather than plain `nvflare` (see details in [the NVFlare docs](https://nvflare.readthedocs.io/en/main/installation.html#optional-dependencies)).
2. Modify your NVFlare configurations to include FHE filters and / or arguments (see examples from NVFlare for [PyTorch](https://github.com/NVIDIA/NVFlare/blob/main/examples/tutorials/self-paced-training/part-3_security_and_privacy/chapter-5_Privacy_In_Federated_Learning/05.3_homomorphic_encryption/05.3.1_privacy_with_homormorphic_encryption.ipynb) and [XGBoost](https://github.com/NVIDIA/NVFlare/tree/main/examples/advanced/xgboost/fedxgb_secure))
3. Enable homomorphic encryption in your Code Runs below
4. You can find a notebook with an example in the [Rhino FCP User-Resources](https://github.com/RhinoHealth/user-resources/tree/main/examples/nvflare/xgboost/xgboost-fhe)

## Enabling homomorphic encryption in Rhino FCP Code Runs

{% stepper %}
{% step %}

#### Navigate to your Code Objects page

{% endstep %}

{% step %}

#### Select “run” for an NVFlare Code Object, expand the configuration options

{% endstep %}

{% step %}

#### Select “enable homomorphic encryption” and enter parameters (use “{}” to use defaults)

{% endstep %}

{% step %}

#### Select “Run Training”

{% endstep %}
{% endstepper %}


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