This paper is in the following e-collection/theme issue:

Machine Learning (1401) Electronic Health Records (971) Adoption and Change Management of eHealth Systems (677) Tools, Programs and Algorithms (246) Ontologies, Classifications, and Coding (227) Ethics, Privacy, and Legal Issues (458)

Published on in Vol 9, No 6 (2021): June

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/26598, first published .
Implementing Vertical Federated Learning Using Autoencoders: Practical Application, Generalizability, and Utility Study

Implementing Vertical Federated Learning Using Autoencoders: Practical Application, Generalizability, and Utility Study

Implementing Vertical Federated Learning Using Autoencoders: Practical Application, Generalizability, and Utility Study

Authors of this article:

Dongchul Cha1, 2 Author Orcid Image ;   MinDong Sung1 Author Orcid Image ;   Yu-Rang Park1 Author Orcid Image
Article Authors Cited by (20) Tweetations (2) Metrics

Journals

  1. Chen Z, Li D, Zhu J, Zhang S. DACFL: Dynamic Average Consensus-Based Federated Learning in Decentralized Sensors Network. Sensors 2022;22(9):3317 View
  2. Prayitno , Shyu C, Putra K, Chen H, Tsai Y, Hossain K, Jiang W, Shae Z. A Systematic Review of Federated Learning in the Healthcare Area: From the Perspective of Data Properties and Applications. Applied Sciences 2021;11(23):11191 View
  3. Joshi M, Pal A, Sankarasubbu M. Federated Learning for Healthcare Domain - Pipeline, Applications and Challenges. ACM Transactions on Computing for Healthcare 2022;3(4):1 View
  4. Rani S, Kataria A, Kumar S, Tiwari P. Federated learning for secure IoMT-applications in smart healthcare systems: A comprehensive review. Knowledge-Based Systems 2023;274:110658 View
  5. Li S, Liu P, Nascimento G, Wang X, Leite F, Chakraborty B, Hong C, Ning Y, Xie F, Teo Z, Ting D, Haddadi H, Ong M, Peres M, Liu N. Federated and distributed learning applications for electronic health records and structured medical data: a scoping review. Journal of the American Medical Informatics Association 2023;30(12):2041 View
  6. Guzzo A, Fortino G, Greco G, Maggiolini M. Data and model aggregation for radiomics applications: Emerging trend and open challenges. Information Fusion 2023;100:101923 View
  7. Sharma S, Guleria K. A comprehensive review on federated learning based models for healthcare applications. Artificial Intelligence in Medicine 2023;146:102691 View
  8. Chen S, Jin T, Xia Y, Li X. Metadata and Image Features Co-Aware Semi-Supervised Vertical Federated Learning With Attention Mechanism. IEEE Transactions on Vehicular Technology 2024;73(2):2520 View
  9. Vo V, Shin T, Yang H, Kang S, Kim S. A comparison between centralized and asynchronous federated learning approaches for survival outcome prediction using clinical and PET data from non-small cell lung cancer patients. Computer Methods and Programs in Biomedicine 2024;248:108104 View
  10. Rauniyar A, Hagos D, Jha D, Håkegård J, Bagci U, Rawat D, Vlassov V. Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directions. IEEE Internet of Things Journal 2024;11(5):7374 View
  11. Zhao Y, Weng J, Liu J, Cai M. Enabling privacy-preserving medication analysis in distributed EHR systems. Journal of Information Security and Applications 2024;82:103749 View
  12. Luzón M, Rodríguez-Barroso N, Argente-Garrido A, Jiménez-López D, Moyano J, Del Ser J, Ding W, Herrera F. A Tutorial on Federated Learning from Theory to Practice: Foundations, Software Frameworks, Exemplary Use Cases, and Selected Trends. IEEE/CAA Journal of Automatica Sinica 2024;11(4):824 View
  13. Oh S, Lee M. Task-Driven Transferred Vertical Federated Deep Learning for Multivariate Internet of Things Time-Series Analysis. Applied Sciences 2024;14(11):4606 View
  14. Liu Y, Kang Y, Zou T, Pu Y, He Y, Ye X, Ouyang Y, Zhang Y, Yang Q. Vertical Federated Learning: Concepts, Advances, and Challenges. IEEE Transactions on Knowledge and Data Engineering 2024;36(7):3615 View
  15. Majeed A, Hwang S. A Multifaceted Survey on Federated Learning: Fundamentals, Paradigm Shifts, Practical Issues, Recent Developments, Partnerships, Trade-Offs, Trustworthiness, and Ways Forward. IEEE Access 2024;12:84643 View

Books/Policy Documents

  1. Stripelis D, Ambite J. Artificial Intelligence for Personalized Medicine. View