> For the complete documentation index, see [llms.txt](https://prep.pathcheck.org/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://prep.pathcheck.org/privacy-preserved-ai-for-healthcare/federated-split-learning.md).

# Federated and Split Learning

![](/files/-MYPubB8PvUxU8Ou-gky)

The final technique is federated and split learning. Jane wants to understand which medical treatment for given comorbidity leads to a successful recovery.  John’s phone trains a local AI to see how his health status and medical treatment lead to some kind of recovery or hospitalization. Although this local AI will be noisy because it only has data from John. If every user starts calculating such local AI and transmits the local AI parameters to the server. The server can then create the master AI by averaging these multiple local AI parameters. 

As you can see, John did not upload his private health data or whether he recovered.

{% embed url="<http://splitlearning.mit.edu>" %}

{% embed url="<https://en.wikipedia.org/wiki/Federated_learning>" %}

**References**<br>

\[1] Vepakomma, Praneeth, et al. “NoPeek: Information Leakage Reduction to Share Activations in Distributed Deep Learning.” ArXiv:2008.09161 \[Cs, Stat], Aug. 2020. arXiv.org, <http://arxiv.org/abs/2008.09161>.