Rebirth of Distributed AI—A Review of eHealth Research

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 applicatio...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2021-07, Vol.21 (15), p.4999
Main Authors: Khan, Manzoor Ahmed, Alkaabi, Najla
Format: Article
Language:English
Subjects:
Artificial intelligence
Communication
data privacy
distributed computing
Domains
eHealth
Electronic health records
federated learning
Health care industry
Internet of Things
Privacy
Review
Smart cities
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Summary: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.
ISSN:1424-8220
1424-8220
DOI:10.3390/s21154999