Abstract
This article presents the architecture, design and validation of a distributed learning approach, that provides support for knowledge preservation. The architecture is able to provide support for Collaborative Data Mining, Context Aware Data Mining, but also Federated Learning. Improving User Experience, providing support for research activities and providing a framework for production-grade machine learning pipeline automations were the primary objectives for the design of the proposed architecture. Third party service support is available out of the box, maintaining the loose-coupling of the system. Obtained results are promising, the system being validated with a use case on Collaborative Data Mining.
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Acknowledgment
This research was made possible by funding from the ICT-AGRI-FOOD 2019 Joint Call. This work was supported by a grant of the Romanian National Authority for Scientific Research and Innovation, CCCDI-UEFISCDI, project number COFUND-ICT-AGRI-FOOD-MUSHNOMICS 205.2020, within PNCDI III.
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Erdei, R., Delinschi, D., Matei, O. (2023). Security Centric Scalable Architecture for Distributed Learning and Knowledge Preservation. In: García Bringas, P., et al. 17th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2022). SOCO 2022. Lecture Notes in Networks and Systems, vol 531. Springer, Cham. https://doi.org/10.1007/978-3-031-18050-7_64
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