====================== Illustrative use cases ====================== These scenarios explain where the architecture may be useful. They are not statements of customer deployments, measured model improvements, or features enabled in every Rizemind configuration. An AI vendor and financial institutions ======================================= Consider a vendor supplying a transaction-monitoring model to several financial institutions. Each institution observes different transactions and edge cases. A federated implementation can keep training against those datasets within each institution. Permitted updates are then aggregated. Rizemind can associate submitted updates with signing identities, measure contributions under the chosen strategy and record selected training events. The vendor can coordinate collaborative model development without requiring a central repository containing every institution's underlying training dataset. The parties still need a compatible task, agreed evaluation metrics and suitable security controls. Improvement is an evaluation result, not an automatic property of federation. Models used by agents ===================== The same architecture may support models used inside agents. Different deployments can produce local training or evaluation signals that organizations do not want to export as source data. Approved training can occur locally while the federation exchanges permitted updates. Depending on the recorded fields, the workflow can associate updates with participants, model versions, rounds and contribution results. See :ref:`model identity and records ` for how to interpret those associations. This is the federated training and verification layer around models that may be used by agents. It is not the agent runtime, an agent-governance platform, or an automatic record of every inference or downstream use. Public-sector and multi-entity organizations ============================================ Several public healthcare institutions might contribute to the same analytical modelling task while maintaining separate source datasets. Similar boundaries can exist between business units, subsidiaries or geographic environments within one organization. A federated architecture can execute approved training locally and aggregate permitted updates. Rizemind adds authentication, attribution and selected training records around that process. Suitability depends on the actual data, model, permissions and deployment controls. Rizemind does not itself establish compliance with healthcare, privacy, residency or other sector requirements. Next steps ========== Review :ref:`the data boundaries ` and :doc:`the integration workflow <../how-tos/flower-integration>`. For a documented technical example, use :doc:`Rizemind on Arc Mainnet <../quickstarts/arc-mainnet>`. The Arc example is separate from the illustrative institutional scenarios above.