Rebirth of distributed AI—A review of ehealth research

Manzoor Ahmed Khan, Najla Alkaabi

Research output: Contribution to journalReview articlepeer-review

Abstract

The envisioned smart city domains are expected to rely heavily on artificial intelligence and machine learning (ML) approaches for their operations, where the basic ingredient is data. Privacy of the data and training time have been major roadblocks to achieving the specific goals of each application domain. Policy makers, the research community, and the industrial sector have been putting their efforts into addressing these issues. Federated learning, with its distributed and local training approach, stands out as a potential solution to these challenges. In this article, we discuss the potential interplay of different technologies and AI for achieving the required features of future smart city services. Having discussed a few use-cases for future eHealth, we list design goals and technical requirements of the enabling technologies. The paper confines its focus on federated learning. After providing the tutorial on federated learning, we analyze the Federated Learning research literature. We also highlight the challenges. A solution sketch and high-level research directions may be instrumental in addressing the challenges.

Original languageEnglish
Article number4999
JournalSensors
Volume21
Issue number15
DOIs
Publication statusPublished - Aug 1 2021

Keywords

  • Data privacy
  • Distributed computing
  • Federated learning
  • eHealth

ASJC Scopus subject areas

  • Analytical Chemistry
  • Information Systems
  • Atomic and Molecular Physics, and Optics
  • Biochemistry
  • Instrumentation
  • Electrical and Electronic Engineering

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