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Related Experiment Videos

Federated Compressed Learning Edge Computing Framework with Ensuring Data Privacy for PM2.5 Prediction in Smart City

Karisma Trinanda Putra1,2, Hsing-Chung Chen1,3, Prayitno1,4

  • 1Department of Computer Science and Information Engineering, Asia University, Taichung City 413, Taiwan.

Sensors (Basel, Switzerland)
|July 20, 2021
PubMed
Summary
This summary is machine-generated.

Federated Compressed Learning (FCL) addresses sparse PM2.5 data in smart cities by reducing data by over 95% while maintaining privacy. This edge computing framework enables secure, efficient air quality prediction in wireless sensing networks.

Keywords:
data privacyfederated compressed learningsmart city sensing

Related Experiment Videos

Area of Science:

  • Environmental Science
  • Computer Science
  • Data Science

Background:

  • Sparse data in PM2.5 air quality monitoring systems challenges smart city sensing applications.
  • Existing centralized systems face data privacy concerns due to exposed sensor data.
  • Inefficient deployment, communication, and fragmented records hinder high-resolution air quality prediction.

Purpose of the Study:

  • To propose a novel edge computing framework, Federated Compressed Learning (FCL), for efficient and private PM2.5 prediction in smart cities.
  • To develop a green energy-based wireless sensing network system utilizing the FCL framework.
  • To validate the performance of FCL through prototype development and testing.

Main Methods:

  • Implementing a framework combining compression techniques, regional joint learning, and secure data exchange.
  • Utilizing edge computing for decentralized data processing and privacy preservation.
  • Developing prototypes for a green energy-based wireless sensing network.

Main Results:

  • Achieved over 95% reduction in data consumption.
  • Maintained an error rate below 5% for PM2.5 predictions.
  • Demonstrated secure data transmission and heavy data compaction in wireless sensing networks.

Conclusions:

  • FCL effectively reduces data quantity and ensures data privacy for PM2.5 prediction in smart city sensing.
  • The framework supports green energy-based wireless sensing networks and software/hardware co-design.
  • While FCL shows slightly lower accuracy than centralized training, its data compaction and security benefits are significant for WSNs.