Abstract
The application of Data Governance (DG) to Federated Machine Learning (FML) could provide a way to produce better Machine Learning models. Nevertheless, such an application is still almost nonexistent in literature. Within a proposal for applying DG to FML, we first present an approach of metadata for FML, to provide accountability and assist with the continuous improvement of models in the federation. Our proposal includes a metadata model for tracing the operations of participants and collecting all information regarding the definition of goals and configuration of FML training processes. Additionally, we present the outline of a metadata management system as part of a broader DG architecture. Finally, we show some use cases of metadata management.
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Funded by the German Federal Ministry of Education and Research. Project name: KIWI, RefNr: 16KIS1142K.
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Peregrina, J.A., Ortiz, G., Zirpins, C. (2022). Towards a Metadata Management System for Provenance, Reproducibility and Accountability in Federated Machine Learning. In: Zirpins, C., et al. Advances in Service-Oriented and Cloud Computing. ESOCC 2022. Communications in Computer and Information Science, vol 1617. Springer, Cham. https://doi.org/10.1007/978-3-031-23298-5_1
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