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federated-learning-profile.md

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# Federated Learning RO-Crate Profile
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*Text in Italics = note or template, remove later*
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Version: 0.1
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Permalink: N/A – use draft link <https://esciencelab.org.uk/federated-learning-ro-crate-profile/federated-learning-profile.html>
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Authors:
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## Compatibility
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This profile is based on RO-Crate 1.2 and aims to be compatible with other profiles used in trusted research environments and workflows, including [Five Safes Crate] (0.4+) and the [Workflow Run RO-Crate] family.
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This profile is based on RO-Crate 1.2 and aims to be compatible with other profiles used in trusted research environments and workflows, including [Five Safes RO-Crate] (0.4+) and the [Workflow Run RO-Crate] family.
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## Inheritance
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This profile inherits all the requirements from [Process Run Crate], a profile designed to capture the execution of one or more computational tools. This ensures consistency in the core metadata structure of the crate.
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To summarise this profile as an extension of Process Run Crate: the [CreateAction] represents the learning process, with [object] referencing the training datasets AND the learning configuration, [result] referencing the output model, and [instrument] referencing the federated learning framework used (e.g. Flower).
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## *Example Metadata Document (`ro-crate-metadata.json`)*
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## Example Metadata Document (`ro-crate-metadata.json`)
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Example metadata file: [JSON-LD](example-fl-crate/ro-crate-metadata.json), [HTML preview](example-fl-crate/ro-crate-preview.html).
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* `@id` SHOULD be a persistent identifier for the dataset
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* [license] SHOULD be included. For public datasets this could be an open license, for restricted or sensitive datasets this can describe the conditions of access
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* [conformsTo] MAY reference a common data model or phenotype dictionary that data in the dataset follows, e.g. OMOP mapping, GA4GH Phenopackets, Frictionless description
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* [subjectOf] MAY reference a contextual or data entity describing a Data Management Plan for the dataset.
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* If [Croissant](https://docs.mlcommons.org/croissant/docs/croissant-spec-1.1.html) metadata is available for the dataset, *TODO describe how this should be included in the crate*
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* *How to describe bias/imbalance? Is there a way to do this in Croissant?*
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* *List of variables used/filtering criteria - minimum information needed to succeed in the experiment*
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* *For GDPR - if the dataset has 100 vars but you only need 20, you only get the 20*
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* *Information about variables in the dataset may be available - is this needed?*
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* [subjectOf] MAY reference a contextual or data entity describing a Data Management Plan for the dataset
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* If [Croissant](https://docs.mlcommons.org/croissant/docs/croissant-spec-1.1.html) metadata is available for the dataset, this MAY also be included
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Each entity representing a training dataset MUST be referenced from [object] on the [CreateAction] which describes the training execution (see [Federated Learning Process Execution](#federated-learning-process-execution))
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Each entity representing a training dataset MUST be referenced from [object] on the [CreateAction] which describes the training execution (see [Federated Learning Process Execution](#federated-learning-process-execution)).
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#### Data partitioning strategy
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[Workflow Run Crate]: https://www.researchobject.org/workflow-run-crate/profiles/workflow_run_crate
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[Provenance Run Crate]: https://www.researchobject.org/workflow-run-crate/profiles/provenance_run_crate
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[Workflow Run RO-Crate]: https://www.researchobject.org/workflow-run-crate
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[Five Safes Crate]: https://trefx.uk/5s-crate/
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[Five Safes RO-Crate]: https://trefx.uk/5s-crate/
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[CreateAction]: http://schema.org/CreateAction
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[object]: http://schema.org/object
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[result]: http://schema.org/result

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