.. _glossary:

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Glossary
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.. glossary::

    **Digital Imaging and Communications in Medicine (DICOM)**
      This is the standard data format for medical imaging information and related data, and enables integration between medical imaging devices and picture archiving and communication systems (PACS).

    **Federated learning**
      Federated learning is a machine learning (ML) technique that trains an algorithm across multiple decentralized edge devices or servers holding local data samples, without exchanging them.

    **Flower Framework**
      Flower is an open-source framework for building federated learning systems. It provides tools and libraries to facilitate the development and deployment of federated learning applications. For more information, please see its `official documentation <https://flower.ai/docs/framework/>`_.

    **NVIDIA FLARE**
      NVIDIA Federated Learning Application Runtime Environment is a domain-agnostic, open-source, extensible SDK that allows researchers and data scientists to adapt existing ML/DL workflows to a federated paradigm.
      For more information, please see the documentation `here <https://nvflare.readthedocs.io/en/main/index.html>`_.

    **OMOP**
      Observational Medical Outcomes Partnership (OMOP). In FLIP documentation, this typically refers to the OMOP Common Data Model (CDM) used to standardise clinical data for federated cohort queries.

    **PACS**
      Picture Archiving and Communication System (PACS), the clinical system used to store and retrieve medical imaging studies (such as DICOM series).

    **RBAC**
      Role Based Access Control (RBAC) defines what users are able to access within the FLIP platform.

    **XNAT**
      XNAT is an open-source imaging informatics platform used in FLIP to store, manage and access imaging data for research workflows. For more information on XNAT, please see the `documentation <https://wiki.xnat.org/documentation>`_.