| Title | FengHuoLun: A Federated Learning based Edge Computing Platform for Cyber-Physical Systems |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Zhang, Chong, Liu, Xiao, Zheng, Xi, Li, Rui, Liu, Huai |
| Conference Name | 2020 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) |
| Keywords | Biological system modeling, cloud computing, Computational modeling, Cyber-physical systems, edge computing, federated learning, human factors, Logistics, Metrics, microservices, Pervasive Computing Security, pubcrawl, Resiliency, Scalability, Servers, Testing, trustworthy |
| Abstract | Cyber-Physical Systems (CPS) such as intelligent connected vehicles, smart farming and smart logistics are constantly generating tons of data and requiring real-time data processing capabilities. Therefore, Edge Computing which provisions computing resources close to the End Devices from the network edge is becoming the ideal platform for CPS. However, it also brings many issues and one of the most prominent challenges is how to ensure the development of trustworthy smart services given the dynamic and distributed nature of Edge Computing. To tackle this challenge, this paper proposes a novel Federated Learning based Edge Computing platform for CPS, named "FengHuoLun". Specifically, based on FengHuoLun, we can: 1) implement smart services where machine learning models are trained in a trusted Federated Learning framework; 2) assure the trustworthiness of smart services where CPS behaviours are tested and monitored using the Federated Learning framework. As a work in progress, we have presented an overview of the FengHuoLun platform and also some preliminary studies on its key components, and finally discussed some important future research directions. |
| DOI | 10.1109/PerComWorkshops48775.2020.9156259 |
| Citation Key | zhang_fenghuolun_2020 |
FengHuoLun: A Federated Learning based Edge Computing Platform for Cyber-Physical Systems