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Cited by 301Privacy-Preserving Technologies in DataSmart Cities and TechnologiesInternet of Things and AI

Federated learning enabled digital twins for smart cities: Concepts, recent advances, and future directions

Open access
Ramu, Swarna Priya, Boopalan, Parimala, Pham, Quoc-Viet, Maddikunta, Praveen Kumar Reddy, Huynh-The, Thien, Alazab, Mamoun, Nguyen, Thanh Thi, Gadekallu, Thippa Reddy·2022·Sustainable Cities and Society
DOI 10.1016/j.scs.2021.103663
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Crossref · 10.1016/j.scs.2021.103663

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Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • The privacy and security issues associated with data analysis within a digital twin are addressed by keeping users' data localized within an FL model, which provides the desired outcomes in terms of implementing data analytics.

    “The privacy and security issues associated with data analysis within a digital twin are addressed by keeping users' data localized within an FL model, which provides the desired outcomes in terms of implementing data analytics [108].”

    From A comprehensive survey on federated learning techniques for healthcare informatics (2023) · cites this paper as [108]

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References 85 · Cited by 301

Authors
Ramu, S. P., Boopalan, P., Pham, Q.-V., Maddikunta, P. K. R., Huynh-The, T., Alazab, M., Nguyen, T. T., & Gadekallu, T. R.
Affiliations
  • Vellore Institute of Technology University (IN)
  • Pusan National University (KR)
  • Charles Darwin University (AU)
  • Deakin University (AU)
Corresponding author
Gadekallu, Thippa Reddy
ORCID
Venue
Sustainable Cities and Society
Publisher
Elsevier BV
Published
2022-04
Volume
79
Pages
103663
ISSN
2210-6707 (ISSN-L)
DOI
10.1016/j.scs.2021.103663
OpenAlex
W4206707323
Semantic Scholar
92041ceccd7bb6f1058cbabb9b34daa1dfc941b1
Cited by
301
References
85
Citations by year
2026: 48 · 2025: 78 · 2024: 69 · 2023: 71 · 2022: 35
FWCI
20.81
Type
journal article
Language
en
Open access
Licence
Locations
  • Sustainable Cities and Society (publishedVersion)
  • Deakin Research Online (Deakin University) (submittedVersion)
  • Figshare (submittedVersion, cc-by)
Funders
  • Ministry of Science and ICT, South Korea (NRF-2019R1C1C1006143)
  • National Research Foundation of Korea
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
  • Physical Sciences › Engineering › Media Technology › Smart Cities and Technologies
  • Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
Keywords
federated learning, digital twin, smart cities, privacy preservation, Internet of Things, smart city applications, data governance, real-time applications

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