Detalle Publicación

ARTÍCULO
ChainFL: a simulation platform for joint federated learning and blockchain in edge/cloud computing environments
Autores: Qu, G.; Cui, N.; Wu, H. (Autor de correspondencia); Li, R.; Ding, Yuemin
Título de la revista: IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
ISSN: 1551-3203
Volumen: 18
Número: 5
Páginas: 3572 - 3581
Fecha de publicación: 2022
Lugar: WOS
Resumen:
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.