Title Collaborative distributed machine learning on blockchain /
Translation of Title Bendradarbiavimu grįstas paskirstytas mašininis mokymasis blokų grandinėje.
Authors Drungilas, Vaidotas
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Pages 188
Keywords [eng] collaborative distributed learning ; private blockchain ; local oracle services
Abstract [eng] Collaborative distributed machine learning approaches are constrained by insufficient trust, limitations imposed by sensitive data, and a complex adaptation process for the currently existing machine learning solutions. A method for collaborative privacy-preserving distributed machine learning has been proposed, thus enabling the blockchain network participants to collaborate via the model deployment process. The proposed method quantifies the contributions of the participants and enables the usage of knowledge accumulated on the blockchain network via the weighted ensemble or the knowledge distillation approaches. Proof-of-concept implementation has been developed by using the Hyperledger Fabric private blockchain network architecture, thus demonstrating the feasibility of collaborative distributed machine learning. The proposed method introduces novel blockchain oracle components that enables the reusability of existing machine learning solutions. The completed experiments measured the performance of the proposed blockchain network architecture, tested the proposed collaboration contribution evaluation strategy and measured the performance impact of knowledge distillation approach. For the tested dataset and classifier configurations the proposed method increased the classifier performance by 4.8% and 1.9% when compared to single model approach.
Dissertation Institution Kauno technologijos universitetas.
Type Doctoral thesis
Language English
Publication date 2024