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.

本文提出了一种将增量学习矢量量化算法（XuILVQ）与以太坊区块链技术结合，以促进分布式环境中的安全高效数据共享、模型训练和原型存储的新型物联网解决方案。通过一系列实验评估我们的系统性能，展示了其在物联网环境中提高机器学习任务准确性和效率的潜力。

面向物联网的协作机器学习的区块链解决方案

A Blockchain Solution for Collaborative Machine Learning over IoT

The proliferation of deep learning techniques led to a wide range of advanced
analytics applications in important business areas such as predictive
maintenance or product recommendation. However, as the effectiveness of
advanced analytics naturally depends on the availability of sufficient data, an
organization's ability to exploit the benefits might be restricted by limited
data or likewise data access. These challenges could force organizations to
spend substantial amounts of money on data, accept constrained analytics
capacities, or even turn into a showstopper for analytics projects. Against
this backdrop, recent advances in deep learning to generate synthetic data may
help to overcome these barriers. Despite its great potential, however,
synthetic data are rarely employed. Therefore, we present a taxonomy
highlighting the various facets of deploying synthetic data for advanced
analytics systems. Furthermore, we identify typical application scenarios for
synthetic data to assess the current state of adoption and thereby unveil
missed opportunities to pave the way for further research.

通过对于合成数据具有的潜力进行分析，本文提出了将合成数据用于高级分析系统的税 onomic，并为其应用场景进行了评估，以揭示继续研究的机会。