Can I read PSDF: Privacy-aware IoV Service Deployment with Federated Learning in Cloud-Edge Computing on EtoBox?

PSDF: Privacy-aware IoV Service Deployment with Federated Learning in Cloud-Edge Computing by Xiaolong Xu; Wentao Liu; Yulan Zhang; Xuyun Zhang; Wanchun Dou; Lianyong Qi; Md Zakirul Alam Bhuiyan is a Computer Science article available to read on EtoBox.

What is PSDF: Privacy-aware IoV Service Deployment with Federated Learning in Cloud-Edge Computing about?

Through the collaboration of cloud and edge, cloud-edge computing allows the edge that approximates end-users undertakes those non-computationally intensive service processing of the cloud, reducing the communication overhead and satisfying the low latency requirement of Internet of Vehicle (IoV). With cloud-edge computing, the computing tasks in IoV is able to be delivered to the edge servers (ESs) instead of the cloud and rely on the deployed services of ESs for a series of processing. Due to the storage and computing resource limits of ESs, how to dynamically deploy partial services to the edge is still a puzzle. Moreover, the decision of service deployment often requires the transmission of local service requests from ESs to the cloud, which increases the risk of privacy leakage. In this article, a method for privacy-aware IoV service deployment with federated learning in cloud-edge computing, named PSDF, is proposed. Technically, federated learning secures the distributed training of deployment decision network on each ES by the exchange and aggregation of model weights, avoiding the original data transmission. Meanwhile, homomorphic encryption is adopted for the uploaded weig

Who reads PSDF: Privacy-aware IoV Service Deployment with Federated Learning in Cloud-Edge Computing?

Author

Xiaolong Xu; Wentao Liu; Yulan Zhang; Xuyun Zhang; Wanchun Dou; Lianyong Qi; Md Zakirul Alam Bhuiyan

Publisher

Association for Computing Machinery (ACM)

Published

2022

Language

EN

Field

Computer Science (Physical Sciences)