【Author】
Qu, Guanjin; Cui, Naichuan; Wu, Huaming; Li, Ruidong; Ding, Yuemin
【Source】IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
【Abstract】As a distributed computing paradigm, edge computing has become a key technology for providing timely services to mobile devices by connecting Internet of Things (IoT), cloud centers, and other facilities. By offloading compute-intensive tasks from IoT devices to edge/cloud servers, the communication and computation pressure caused by the massive data in Industrial IoT can be effectively reduced. In the process of computation offloading in edge computing, it is critical to dynamically make optimal offloading decisions to minimize the delay and energy consumption spent on the devices. Although there are a large number of task offloading-decision models, how to measure and evaluate the quality of different models and configurations is crucial. In this article, we propose a novel simulation platform named ChainFL, which can build an edge computing environment among IoT devices while being compatible with federated learning and blockchain technologies to better support the embedding of security-focused offloading algorithms. ChainFL is lightweight and compatible, and it can quickly build complex network environments by connecting devices of different architectures. Moreover, due to its distributed nature, ChainFL can also be deployed as a federated learning platform across multiple devices to enable federated learning with high security due to its embedded blockchain. Finally, we validate the versatility and effectiveness of ChainFL by embedding a complex offloading-decision model in the platform, and deploying it in an Industrial IoT environment with security risks.
【Keywords】Computational modeling; Cloud computing; Collaborative work; Task analysis; Blockchains; Edge computing; Servers; Blockchain; computation offloading; edge computing; federated learning; simulation platform
【标题】ChainFL：边缘/云计算环境中联合联邦学习和区块链的模拟平台
【摘要】作为一种分布式计算范式，边缘计算已成为通过连接物联网 (IoT)、云中心和其他设施为移动设备提供及时服务的关键技术。通过将计算密集型任务从物联网设备卸载到边缘/云服务器，可以有效降低工业物联网中海量数据带来的通信和计算压力。在边缘计算的计算卸载过程中，动态地做出最优卸载决策以最小化设备上的延迟和能耗至关重要。尽管有大量的任务卸载决策模型，但如何衡量和评估不同模型和配置的质量至关重要。在本文中，我们提出了一个名为 ChainFL 的新型仿真平台，它可以在物联网设备之间构建边缘计算环境，同时兼容联邦学习和区块链技术，以更好地支持以安全为中心的卸载算法的嵌入。 ChainFL 轻量级和兼容，可以通过连接不同架构的设备快速构建复杂的网络环境。此外，由于其分布式特性，ChainFL 还可以部署为跨多个设备的联邦学习平台，以通过其嵌入式区块链实现高安全性的联邦学习。最后，我们通过在平台中嵌入复杂的卸载决策模型，并将其部署在存在安全风险的工业物联网环境中，验证了 ChainFL 的多功能性和有效性。
【关键词】计算建模；云计算;协作工作；任务分析；区块链；边缘计算；服务器；区块链；计算卸载；边缘计算；联邦学习；仿真平台
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