Empowering Edge Intelligence by Air-Ground Integrated Federated Learning

Yuben Qu, Chao Dong, Jianchao Zheng, Haipeng Dai, Fan Wu, Song Guo, Alagan Anpalagan

Research output: Journal article publicationJournal articleAcademic researchpeer-review

5 Citations (Scopus)

Abstract

Ubiquitous intelligence has been widely recognized as a critical vision of the future sixth generation (6G) networks, which implies intelligence over the whole network from the core to the edge, including end devices. Nevertheless, fulfilling this vision, particularly the intelligence at the edge, is extremely challenging due to the limited resources of edge devices as well as the ubiquitous coverage envisioned by 6G. To empower edge intelligence, in this article, we propose a framework called air-ground integrated federated learning (AGIFL), which organically integrates air-ground integrated networks and federated learning (FL). In AGIFL, leveraging the flexible on-demand 3D deployment of aerial nodes such as unmanned aerial vehicles (UAVs), all the nodes can collaboratively train an effective learning model by FL. We also conduct a case study to evaluate the effect of two different deployment schemes of UAVs on learning and network performance. Last but not least, we highlight several technical challenges and future research directions in AGIFL.

Original languageEnglish
Pages (from-to)34-41
Number of pages8
JournalIEEE Network
Volume35
Issue number5
DOIs
Publication statusPublished - 1 Sep 2021

ASJC Scopus subject areas

  • Software
  • Information Systems
  • Hardware and Architecture
  • Computer Networks and Communications

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