A Blockchain Solution for Collaborative Machine Learning over IoT
Carlos Beis-Penedo Francisco Troncoso-Pastoriza Rebeca P. Díaz-Redondo Ana Fernández-Vilas Manuel Fernández-Veiga Martín González Soto
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.