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

Generative Artificial Intelligence (GAI) possesses the capabilities of
generating realistic data and facilitating advanced decision-making. By
integrating GAI into modern Internet of Things (IoT), Generative Internet of
Things (GIoT) is emerging and holds immense potential to revolutionize various
aspects of society, enabling more efficient and intelligent IoT applications,
such as smart surveillance and voice assistants. In this article, we present
the concept of GIoT and conduct an exploration of its potential prospects.
Specifically, we first overview four GAI techniques and investigate promising
GIoT applications. Then, we elaborate on the main challenges in enabling GIoT
and propose a general GAI-based secure incentive mechanism framework to address
them, in which we adopt Generative Diffusion Models (GDMs) for incentive
mechanism designs and apply blockchain technologies for secure GIoT management.
Moreover, we conduct a case study on modern Internet of Vehicle traffic
monitoring, which utilizes GDMs to generate effective contracts for
incentivizing users to contribute sensing data with high quality. Finally, we
suggest several open directions worth investigating for the future popularity
of GIoT.

通过将生成式人工智能（GAI）与现代物联网（IoT）相结合，我们提出了生成式物联网（GIoT）的概念，并研究了其潜在前景。我们介绍了四种 GAI 技术，讨论了 GIoT 的应用，并提出了一个基于 GAI 的安全激励机制框架，使用生成扩散模型（GDMs）进行机制设计，并应用区块链技术进行安全的 GIoT 管理。最后，我们通过现代车辆的交通监测案例研究，展示了利用 GDMs 生成有效合同来激励用户提供高质量感知数据。未来还有一些值得研究的开放方向，以促进 GIoT 的普及。

从生成式人工智能到生成式物联网：基础、框架与展望

From Generative AI to Generative Internet of Things: Fundamentals,  Framework, and Outlooks

Programming has always been at the heart of technological innovation in the
21st century. With the advent of blockchain technologies and the proliferation
of web3 paradigms of decentralised applications, smart contracts have been very
instrumental in enabling developers to build applications that reside on
decentralised blockchains. Despite the huge interest and potential of smart
contracts, there is still a significant knowledge and skill gap that developers
need to cross in order to build web3 applications. In light of this, we
introduce MazzumaGPT, a large language model that has been optimised to
generate smart contract code and aid developers to scaffold development and
improve productivity. As part of this research, we outline the optimisation and
fine-tuning parameters, evaluate the model's performance on functional
correctness and address the limitations and broader impacts of our research.

MazzumaGPT 是一种经过优化、可生成智能合约代码的大型语言模型，旨在帮助开发人员构建 Web3 应用并提高生产力。本研究介绍了模型的优化和微调参数，评估了其在功能正确性上的性能，并讨论了我们研究的局限性和广泛影响。