Privacy-Preserving Blockchain-Based Federated Learning for Marine Internet of Things

【Author】 Qin, Zhenquan; Ye, Jin; Meng, Jie; Lu, Bingxian; Wang, Lei

【Source】IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS

【Abstract】The marine Internet of things (MIoT) is the application of the Internet of things technology in the marine field. Nowadays, with the arrival of the era of big data, the MIoT architecture has been transformed from cloud computing architecture to edge computing architecture. However, due to the lack of trust among edge computing participants, new solutions with higher security need to be proposed. In the current solutions, some use blockchain technology to solve data security problems while some use federated learning technology to solve privacy problems, but these methods neither combine with the special environment of the ocean nor consider the security of task publishers. In this article, we propose a secure sharing method of MIoT data under an edge computing framework based on federated learning and blockchain technology. Combining its special distributed architecture with the MIoT edge computing architecture, federated learning ensures the privacy of nodes. The blockchain serves as a decentralized way, which stores federated learning workers to achieve nontampering and security. We propose a concept of quality and reputation as the metrics of selection for federated learning workers. Meanwhile, we design a quality proof mechanism [proof of quality (PoQ)] and apply it to the blockchain, making the edge nodes recorded in the blockchain more high-quality. In addition, a marine environment model is built in this article, and the analysis based on this model makes the method proposed in this article more applicable to the marine environment. The numerical results obtained from the simulation experiments clearly show that the proposed scheme can significantly improve the learning accuracy under the premise of ensuring the safety and reliability of the marine environment.

【Keywords】Blockchains; Collaborative work; Edge computing; Task analysis; Computational modeling; Reliability; Data privacy; Blockchain; edge computing; federated learning; marine Internet of things (MIoT); privacy

【标题】保护隐私的基于区块链的海洋物联网联邦学习

【摘要】海洋物联网（MIoT）是物联网技术在海洋领域的应用。如今，随着大数据时代的到来，MIoT架构已经从云计算架构向边缘计算架构转变。然而，由于边缘计算参与者之间缺乏信任，需要提出具有更高安全性的新解决方案。目前的解决方案中，有的使用区块链技术解决数据安全问题，有的使用联邦学习技术解决隐私问题，但这些方法既没有结合海洋的特殊环境，也没有考虑任务发布者的安全问题。在本文中，我们提出了一种基于联邦学习和区块链技术的边缘计算框架下的 MIoT 数据安全共享方法。将其特殊的分布式架构与 MIoT 边缘计算架构相结合，联邦学习确保了节点的隐私。区块链作为一种去中心化的方式，存储联邦学习工作者以实现不可篡改和安全。我们提出了质量和声誉的概念作为联邦学习工作者的选择指标。同时，我们设计了一种质量证明机制[proof of quality (PoQ)]并将其应用到区块链中，使得记录在区块链中的边缘节点更加优质。此外，本文还建立了一个海洋环境模型，基于该模型的分析使得本文提出的方法更适用于海洋环境。仿真实验得到的数值结果清楚地表明，该方案在保证海洋环境安全可靠的前提下，能够显着提高学习精度。

【关键词】区块链；协作工作；边缘计算；任务分析；计算建模；可靠性;数据隐私；区块链；边缘计算；联邦学习；海洋物联网（MIoT）；隐私

【发表时间】2022

【收录时间】2022-07-06

【文献类型】Article

【论文大主题】区块链联邦学习

【论文小主题】两者结合

【影响因子】4.747

【翻译者】石东瑛