DEEP-FEL : Decentralized, Efficient and Privacy-Enhanced Federated Edge Learning for Healthcare Cyber Physical Systems

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

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Author(s)

  • Zhuotao Lian
  • Qinglin Yang
  • Qingkui Zeng
  • Mamoun Alazab
  • Hong Zhao
  • Chunhua Su

Related Research Unit(s)

Detail(s)

Original languageEnglish
Pages (from-to)3558-3569
Journal / PublicationIEEE Transactions on Network Science and Engineering
Volume9
Issue number5
Online published20 May 2022
Publication statusPublished - Sept 2022

Abstract

The rapid development of Internet of Things (IoT) stimulates the innovation for the health-related devices such as remote patient monitoring, connected inhalers and ingestible sensors. Simultaneously, with the aid of numerous equipments, a great number of collected data can be used for disease prediction or diagnosis model establishment. However, the potential patient data leak will also bring privacy and security issues in the interaction period. To deal with these existing issues, we propose a decentralized, efficient, and privacy-enhanced federated edge learning system called DEEP-FEL, which enables medical devices in different institutions to collaboratively train a global model without raw data mutual exchange. Firstly, we design a hierarchical ring topology to alleviate centralization of the conventional training framework, and formulate the ring construction as an optimization problem, which can be solved by an efficient heuristic algorithm. Subsequently, we design an efficient parameter aggregation algorithm for distributed medical institutions to generate a new global model, and the total amount of data transmitted by N nodes is only 2/N times that of traditional algorithm. In addition, data security among different medical institutions is enhanced by adding artificial noise to the edge model. Finally, experimental results on three medical datasets demonstrate the superiority of our system.

Research Area(s)

  • Cyber physical systems, decentralized system, differential privacy, federated learning, mobile healthcare

Citation Format(s)

DEEP-FEL: Decentralized, Efficient and Privacy-Enhanced Federated Edge Learning for Healthcare Cyber Physical Systems. / Lian, Zhuotao; Yang, Qinglin; Wang, Weizheng et al.
In: IEEE Transactions on Network Science and Engineering, Vol. 9, No. 5, 09.2022, p. 3558-3569.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review