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

Artificial Intelligence (1214) Clinical Information and Decision Making (1339) New Methods (911) Machine Learning (1400)

Published on in Vol 22, No 10 (2020): October

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/20891, first published .
Federated Learning on Clinical Benchmark Data: Performance Assessment

Federated Learning on Clinical Benchmark Data: Performance Assessment

Federated Learning on Clinical Benchmark Data: Performance Assessment

Authors of this article:

Geun Hyeong Lee1 Author Orcid Image ;   Soo-Yong Shin1, 2, 3 Author Orcid Image
Article Authors Cited by (65) Tweetations (8) Metrics

Journals

  1. Mazzeo V, Rapisarda A, Giuffrida G. Detection of Fake News on COVID-19 on Web Search Engines. Frontiers in Physics 2021;9 View
  2. Abreha H, Hayajneh M, Serhani M. Federated Learning in Edge Computing: A Systematic Survey. Sensors 2022;22(2):450 View
  3. Budrionis A, Miara M, Miara P, Wilk S, Bellika J. Benchmarking PySyft Federated Learning Framework on MIMIC-III Dataset. IEEE Access 2021;9:116869 View
  4. Nakagawa K, Moukheiber L, Celi L, Patel M, Mahmood F, Gondim D, Hogarth M, Levenson R. AI in Pathology: What could possibly go wrong?. Seminars in Diagnostic Pathology 2023;40(2):100 View
  5. Ng W, Zhang S, Wang Z, Ong C, Gunasekeran D, Lim G, Zheng F, Tan S, Tan G, Rim T, Schmetterer L, Ting D. Updates in deep learning research in ophthalmology. Clinical Science 2021;135(20):2357 View
  6. Drainakis G, Pantazopoulos P, Katsaros K, Sourlas V, Amditis A, Kaklamani D. From centralized to Federated Learning: Exploring performance and end-to-end resource consumption. Computer Networks 2023;225:109657 View
  7. Yoon J, Pinsky M, Clermont G. Artificial Intelligence in Critical Care Medicine. Critical Care 2022;26(1) View
  8. He S, Leanse L, Feng Y. Artificial intelligence and machine learning assisted drug delivery for effective treatment of infectious diseases. Advanced Drug Delivery Reviews 2021;178:113922 View
  9. Zhang L, Vashisht H, Totev A, Trinh N, Ward T. A comparison of distributed machine learning methods for the support of “many labs” collaborations in computational modeling of decision making. Frontiers in Psychology 2022;13 View
  10. Oh W, Nadkarni G. Federated Learning in Health care Using Structured Medical Data. Advances in Kidney Disease and Health 2023;30(1):4 View
  11. Zhu X, Wang D, Pedrycz W, Li Z. Horizontal Federated Learning of Takagi–Sugeno Fuzzy Rule-Based Models. IEEE Transactions on Fuzzy Systems 2022;30(9):3537 View
  12. Chen X, Cheng G, Wang F, Tao X, Xie H, Xu L. Machine and cognitive intelligence for human health: systematic review. Brain Informatics 2022;9(1) View
  13. Li H, Li C, Wang J, Yang A, Ma Z, Zhang Z, Hua D. Review on security of federated learning and its application in healthcare. Future Generation Computer Systems 2023;144:271 View
  14. Sáinz-Pardo Díaz J, López García Á. Study of the performance and scalability of federated learning for medical imaging with intermittent clients. Neurocomputing 2023;518:142 View
  15. Zanotto B, Beck da Silva Etges A, dal Bosco A, Cortes E, Ruschel R, De Souza A, Andrade C, Viegas F, Canuto S, Luiz W, Ouriques Martins S, Vieira R, Polanczyk C, André Gonçalves M. Stroke Outcome Measurements From Electronic Medical Records: Cross-sectional Study on the Effectiveness of Neural and Nonneural Classifiers. JMIR Medical Informatics 2021;9(11):e29120 View
  16. Zheng Z, Zhou Y, Sun Y, Wang Z, Liu B, Li K. Applications of federated learning in smart cities: recent advances, taxonomy, and open challenges. Connection Science 2022;34(1):1 View
  17. Sun L, Wu J. A Scalable and Transferable Federated Learning System for Classifying Healthcare Sensor Data. IEEE Journal of Biomedical and Health Informatics 2023;27(2):866 View
  18. Ogundokun R, Misra S, Maskeliunas R, Damasevicius R. A Review on Federated Learning and Machine Learning Approaches: Categorization, Application Areas, and Blockchain Technology. Information 2022;13(5):263 View
  19. Yu B, Mao W, Lv Y, Zhang C, Xie Y. A survey on federated learning in data mining. WIREs Data Mining and Knowledge Discovery 2022;12(1) View
  20. Naresh V, Thamarai M. Privacy‐preserving data mining and machine learning in healthcare: Applications, challenges, and solutions. WIREs Data Mining and Knowledge Discovery 2023;13(2) View
  21. Lim J, Hong M, Lam W, Zhang Z, Teo Z, Liu Y, Ng W, Foo L, Ting D. Novel technical and privacy-preserving technology for artificial intelligence in ophthalmology. Current Opinion in Ophthalmology 2022;33(3):174 View
  22. Zhang J, Zhu H, Wang F, Zhao J, Xu Q, Li H, Wang Z. Security and Privacy Threats to Federated Learning: Issues, Methods, and Challenges. Security and Communication Networks 2022;2022:1 View
  23. Al-Huthaifi R, Li T, Huang W, Gu J, Li C. Federated learning in smart cities: Privacy and security survey. Information Sciences 2023;632:833 View
  24. Zhu J, Luo J, Ma Y. Screening of serum exosome markers for colorectal cancer based on Boruta and multi-cluster feature selection algorithms. Molecular & Cellular Toxicology 2024;20(2):343 View
  25. Bozkurt C, Aşuroğlu T. Mortality Prediction of Various Cancer Patients via Relevant Feature Analysis and Machine Learning. SN Computer Science 2023;4(3) View
  26. Chaddad A, Lu Q, Li J, Katib Y, Kateb R, Tanougast C, Bouridane A, Abdulkadir A. Explainable, Domain-Adaptive, and Federated Artificial Intelligence in Medicine. IEEE/CAA Journal of Automatica Sinica 2023;10(4):859 View
  27. Brauneck A, Schmalhorst L, Kazemi Majdabadi M, Bakhtiari M, Völker U, Baumbach J, Baumbach L, Buchholtz G. Federated Machine Learning, Privacy-Enhancing Technologies, and Data Protection Laws in Medical Research: Scoping Review. Journal of Medical Internet Research 2023;25:e41588 View
  28. Shen A, Francisco L, Sen S, Tewari A. Exploring the Relationship Between Privacy and Utility in Mobile Health: Algorithm Development and Validation via Simulations of Federated Learning, Differential Privacy, and External Attacks. Journal of Medical Internet Research 2023;25:e43664 View
  29. Lee G, Park J, Kim J, Kim Y, Choi B, Park R, Rhee S, Shin S. Feasibility Study of Federated Learning on the Distributed Research Network of OMOP Common Data Model. Healthcare Informatics Research 2023;29(2):168 View
  30. Khan Q, Khan A, Rizwan A, Ahmad R, Khan S, Kim D. Decentralized Machine Learning Training: A Survey on Synchronization, Consolidation, and Topologies. IEEE Access 2023;11:68031 View
  31. Takahashi K, Yamamoto K, Kuchiba A, Shintani A, Koyama T. Hypothesis testing procedure for binary and multi‐class F1‐scores in the paired design. Statistics in Medicine 2023;42(23):4177 View
  32. Navaz A, Serhani M, El Kassabi H, Taleb I. Empowering Patient Similarity Networks through Innovative Data-Quality-Aware Federated Profiling. Sensors 2023;23(14):6443 View
  33. Mali B, Saha S, Brahma D, Pinninti R, Singh P. Towards Building a Global Robust Model for Heart Disease Detection. SN Computer Science 2023;4(5) View
  34. Liang X, Zhao J, Chen Y, Bandara E, Shetty S. Architectural Design of a Blockchain-Enabled, Federated Learning Platform for Algorithmic Fairness in Predictive Health Care: Design Science Study. Journal of Medical Internet Research 2023;25:e46547 View
  35. Liu Y. Depression clinical detection model based on social media: a federated deep learning approach. The Journal of Supercomputing 2024;80(6):7931 View
  36. Zhang N, Ma Q, Chen X. Enabling Long-Term Cooperation in Cross-Silo Federated Learning: A Repeated Game Perspective. IEEE Transactions on Mobile Computing 2023;22(7):3910 View
  37. Luo G, Liu T, Lu J, Chen X, Yu L, Wu J, Chen D, Cai W. Influence of Data Distribution on Federated Learning Performance in Tumor Segmentation. Radiology: Artificial Intelligence 2023;5(3) View
  38. Chang H, Yu J, Lee G, Heo S, Lee S, Hwang S, Yoon H, Cha W, Shin T, Sim M, Jo I, Kim T. Clinical support system for triage based on federated learning for the Korea triage and acuity scale. Heliyon 2023;9(8):e19210 View
  39. Naresh V, Thamarai M, Allavarpu V. Privacy-preserving deep learning in medical informatics: applications, challenges, and solutions. Artificial Intelligence Review 2023;56(S1):1199 View
  40. Matschinske J, Späth J, Bakhtiari M, Probul N, Kazemi Majdabadi M, Nasirigerdeh R, Torkzadehmahani R, Hartebrodt A, Orban B, Fejér S, Zolotareva O, Das S, Baumbach L, Pauling J, Tomašević O, Bihari B, Bloice M, Donner N, Fdhila W, Frisch T, Hauschild A, Heider D, Holzinger A, Hötzendorfer W, Hospes J, Kacprowski T, Kastelitz M, List M, Mayer R, Moga M, Müller H, Pustozerova A, Röttger R, Saak C, Saranti A, Schmidt H, Tschohl C, Wenke N, Baumbach J. The FeatureCloud Platform for Federated Learning in Biomedicine: Unified Approach. Journal of Medical Internet Research 2023;25:e42621 View
  41. Zukaib U, Cui X, Hassan M, Harris S, Hadi H, Zheng C. Blockchain and Machine Learning in EHR Security: A Systematic Review. IEEE Access 2023;11:130230 View
  42. Tran A, Luong T, Huynh V. A comprehensive survey and taxonomy on privacy-preserving deep learning. Neurocomputing 2024;576:127345 View
  43. Paragliola G, Ribino P. Exploring heterogeneous data distribution issues in e-health federated systems. Biomedical Signal Processing and Control 2024;92:106039 View
  44. Kjwan A, Mohammad O. Enhancing Medical Data Analysis with Federated Learning in the Internet of Medical Things. International Journal of Research In Science & Engineering 2024;(43):38 View
  45. Coutinho-Almeida J, Cruz-Correia R, Rodrigues P. Evaluating distributed-learning on real-world obstetrics data: comparing distributed, centralized and local models. Scientific Reports 2024;14(1) View
  46. Saha S, Hota A, Chattopadhyay A, Nag A, Nandi S. A multifaceted survey on privacy preservation of federated learning: progress, challenges, and opportunities. Artificial Intelligence Review 2024;57(7) View
  47. Grzybowski A, Jin K, Zhou J, Pan X, Wang M, Ye J, Wong T. Retina Fundus Photograph-Based Artificial Intelligence Algorithms in Medicine: A Systematic Review. Ophthalmology and Therapy 2024;13(8):2125 View

Books/Policy Documents

  1. Van Landuyt D, Joosen W. Software Architecture. View
  2. Yoon J, Pinsky M, Clermont G. Annual Update in Intensive Care and Emergency Medicine 2022. View
  3. Ramesh V, S. H, Sundaram S, N. B. P, G. R. H. Handbook of Research on Design, Deployment, Automation, and Testing Strategies for 6G Mobile Core Network. View
  4. Singh A, Kumar A, Choi B. Intelligent Human Computer Interaction. View
  5. Georgoutsos A, Kerasiotis P, Kantere V. Web Information Systems Engineering – WISE 2023. View
  6. Guerra-Manzanares A, Lopez L, Maniatakos M, Shamout F. Trustworthy Machine Learning for Healthcare. View
  7. Hannemann A, Ewald J, Seeger L, Buchmann E. Computational Science – ICCS 2024. View