Fedvision: Federated video analytics with edge computing

Research output: Contribution to journalArticlepeer-review

23 Scopus citations

Abstract

Widely deployed smart cameras are generating a large amount of video data and capable of processing frames on devices. Empowered by edge computing, the video data can also be offloaded to edge servers for processing. By leveraging the on-device processing and computation offloading, we propose a federated video analytics system named FedVision to efficiently provision video analytics across devices and servers. The challenge of designing FedVision is to optimally use the computing and networking resources for video analytics. Since there is no closed-form expression of the system performance, black-box optimization is employed to optimize the system performance. However, using black-box optimization directly incurs excessive system queries that lead to very poor system performance. To solve this problem, we design a new optimization method that integrates black-box optimization with Neural Processes (NPs) as a system performance approximator. This method allows black-box optimizer to query NPs instead of the real system. We validate the performance of FedVision and the new optimization method using both numerical results and experiments with a testbed.

Original languageEnglish (US)
Article number9097917
Pages (from-to)62-72
Number of pages11
JournalIEEE Open Journal of the Computer Society
Volume1
Issue number1
DOIs
StatePublished - 2020

All Science Journal Classification (ASJC) codes

  • General Computer Science

Keywords

  • Black-box optimization
  • Edge computing
  • Machine learning
  • Neural process
  • Video analytics

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