A Blockchain Solution for Collaborative Machine Learning over IoT
Fuente:
arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| author | Beis-Penedo, Carlos Troncoso-Pastoriza, Francisco Díaz-Redondo, Rebeca P. Fernández-Vilas, Ana Fernández-Veiga, Manuel Soto, Martín González |
| author_facet | Beis-Penedo, Carlos Troncoso-Pastoriza, Francisco Díaz-Redondo, Rebeca P. Fernández-Vilas, Ana Fernández-Veiga, Manuel Soto, Martín González |
| contents | The rapid growth of Internet of Things (IoT) devices and applications has led to an increased demand for advanced analytics and machine learning techniques capable of handling the challenges associated with data privacy, security, and scalability. Federated learning (FL) and blockchain technologies have emerged as promising approaches to address these challenges by enabling decentralized, secure, and privacy-preserving model training on distributed data sources. In this paper, we present a novel IoT solution that combines the incremental learning vector quantization algorithm (XuILVQ) with Ethereum blockchain technology to facilitate secure and efficient data sharing, model training, and prototype storage in a distributed environment. Our proposed architecture addresses the shortcomings of existing blockchain-based FL solutions by reducing computational and communication overheads while maintaining data privacy and security. We assess the performance of our system through a series of experiments, showcasing its potential to enhance the accuracy and efficiency of machine learning tasks in IoT settings. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_14136 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | A Blockchain Solution for Collaborative Machine Learning over IoT Beis-Penedo, Carlos Troncoso-Pastoriza, Francisco Díaz-Redondo, Rebeca P. Fernández-Vilas, Ana Fernández-Veiga, Manuel Soto, Martín González Machine Learning Cryptography and Security Networking and Internet Architecture The rapid growth of Internet of Things (IoT) devices and applications has led to an increased demand for advanced analytics and machine learning techniques capable of handling the challenges associated with data privacy, security, and scalability. Federated learning (FL) and blockchain technologies have emerged as promising approaches to address these challenges by enabling decentralized, secure, and privacy-preserving model training on distributed data sources. In this paper, we present a novel IoT solution that combines the incremental learning vector quantization algorithm (XuILVQ) with Ethereum blockchain technology to facilitate secure and efficient data sharing, model training, and prototype storage in a distributed environment. Our proposed architecture addresses the shortcomings of existing blockchain-based FL solutions by reducing computational and communication overheads while maintaining data privacy and security. We assess the performance of our system through a series of experiments, showcasing its potential to enhance the accuracy and efficiency of machine learning tasks in IoT settings. |
| title | A Blockchain Solution for Collaborative Machine Learning over IoT |
| topic | Machine Learning Cryptography and Security Networking and Internet Architecture |
| url | https://arxiv.org/abs/2311.14136 |