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.

翻译：物联网设备及应用的快速增长，使得对能够处理数据隐私、安全性和可扩展性挑战的高级分析及机器学习技术的需求日益增加。联邦学习与区块链技术通过实现分布式数据源上的去中心化、安全且保护隐私的模型训练，已成为应对这些挑战的有效途径。本文提出一种新型物联网解决方案，该方案将增量学习向量量化算法与以太坊区块链技术相结合，以实现在分布式环境中安全高效的数据共享、模型训练及原型存储。所提出的架构通过降低计算与通信开销，同时保持数据隐私与安全性，弥补了现有基于区块链的联邦学习解决方案的不足。通过一系列实验评估系统性能，展示了其在提升物联网场景中机器学习任务准确性与效率方面的潜力。