Abstract
This paper looks into the field of blockchain-based federated learning, an area that’s been studied a lot but is often too abstract for real-world use. Most solutions stay inside academic discussions, and there’s a lack of practical, usable options. Our work introduces a new system that uses blockchain for federated learning in a straightforward, real-world way. Our system includes novel features such as an innovative anti-plagiarism mechanism and a unique consensus method for validating training results. We explain our methods, main results, and what our work could mean for the future in the paper. Our study could help make blockchain-based federated learning more useful by changing how data is shared and learned, all while keeping privacy and decentralization. This could drive more work into creating usable solutions in this field.
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Ferenczi, A., Bădică, C. (2023). A Fully Decentralized Privacy-Enabled Federated Learning System. In: Nguyen, N.T., et al. Computational Collective Intelligence. ICCCI 2023. Lecture Notes in Computer Science(), vol 14162. Springer, Cham. https://doi.org/10.1007/978-3-031-41456-5_34
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DOI: https://doi.org/10.1007/978-3-031-41456-5_34
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