FengHuoLun: a federated learning based edge computing platform for cyber-physical systems

Chong Zhang, Xiao Liu, Xi Zheng, Rui Li, Huai Liu

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

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.
Original languageEnglish
Title of host publication2020 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)
Place of PublicationPiscataway, NJ
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages4
ISBN (Electronic)9781728147161
DOIs
Publication statusPublished - 2020
EventIEEE International Conference on Pervasive Computing and Communications (2020 : 18th) - Austin, United States
Duration: 23 Mar 202027 Mar 2020

Conference

ConferenceIEEE International Conference on Pervasive Computing and Communications (2020 : 18th)
Abbreviated titlePerCom 2020
CountryUnited States
CityAustin
Period23/03/2027/03/20

Keywords

  • Federated Learning
  • Edge Computing
  • Cyber-Physical Systems
  • Trustworthy
  • Microservices

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