Muse ED, Barrett PM, Steinhubl SR, Topol EJ. Towards a smart medical home. Lancet. 2017;389(10067):358. https://doi.org/10.1016/S0140-6736(17)30154-X.

Article  Google Scholar 

Fotouhi M, Bayat M, Das AK, Far HAN, Pournaghi SM, Doostari M-A. A lightweight and secure two-factor authentication scheme for wireless body area networks in health-care iot. Comput Netw. 2020;177:107333. https://doi.org/10.1016/j.comnet.2020.107333.

Article  Google Scholar 

Ding Y, Xu H, Zhao M, Liang H, Wang Y. Group authentication and key distribution for sensors in wireless body area network. Int J Distrib Sensor Netw. 2021;17(9):15501477211044338. https://doi.org/10.1177/1550147721104433.

Article  Google Scholar 

Nguyen DC, Pham Q-V, Pathirana PN, Ding M, Seneviratne A, Lin Z, Dobre O, Hwang W-J. Federated learning for smart healthcare: a survey. ACM Computing Surveys. 2022;55(3):1–37. https://doi.org/10.1145/3501296.

Article  Google Scholar 

Hassan Naqvi. An automated system for classification of chronic obstructive pulmonary disease and pneumonia patients using lung sound analysis. Sensors. 2020;6512:22.

Google Scholar 

Zhao L, Xie H, Zhong L, Wang Y Multi-server verifiable aggregation for federated learning in securing industrial iot. In: 2024 IEEE 27th International Conference on Computer Supported Cooperative Work in Design (CSCWD) 2024. IEEE

Che Z, Purushotham S, Khemani R, Liu Y Interpretable deep models for icu outcome prediction. In: AMIA Annual Symposium Proceedings, American Medical Informatics Association; 2016. pp. 371–380

Rajpurkar P, Irvin J, Zhu K, Yang B, Mehta H, Duan T, Ding D, Bagul A, Langlotz C, Shpanskaya K. Chexnet Radiologist-level pneumonia detection on chest x-rays with deep learning. [Preprint] 2017. Available from https://doi.org/10.48550/arXiv.1711.05225.

Yang C, Rangarajan A, Ranka S, Global model interpretation via recursive partitioning. In: 2018 IEEE 20th International Conference on High Performance Computing and Communications; IEEE 16th International Conference on Smart City; IEEE 4th International Conference on Data Science and Systems (HPCC/SmartCity/DSS), 2018:1563–1570 . https://doi.org/10.1109/HPCC/SmartCity/DSS.2018.00256 . IEEE

Arvaniti E, Fricker KS, Moret M, Rupp N, Hermanns T, Fankhauser C, Wey N, Wild PJ, Rüschoff JH, Claassen M. Automated gleason grading of prostate cancer tissue microarrays via deep learning. Sci Rep. 2018;8(1):12054. https://doi.org/10.1038/s41598-018-30535-1.

Article  Google Scholar 

Khan SH, Alam MGR, A federated learning approach to pneumonia detection. In: 2021 International Conference on Engineering and Emerging Technologies (ICEET), 2021; 1–6. https://doi.org/10.1109/ICEET53442.2021.9659591 . IEEE

Lee H, Chai YJ, Joo H, Lee K, Hwang JY, Kim S-M, Kim K, Nam I-C, Choi JY, Yu HW. Federated learning for thyroid ultrasound image analysis to protect personal information: validation study in a real health care environment. JMIR Med Inform. 2021;9(5):25869. https://doi.org/10.2196/25869.

Article  Google Scholar 

Raza A, Tran KP, Koehl L, Li S. Designing ecg monitoring healthcare system with federated transfer learning and explainable ai. Knowl-Based Syst. 2022;236:107763. https://doi.org/10.1016/j.knosys.2021.107763.

Article  Google Scholar 

Abid R, Rizwan M, Alabdulatif A, Alnajim A, Alamro M, Azrour M. Adaptation of federated explainable artificial intelligence for efficient and secure e-healthcare systems. CMC-Comput Mater Contin. 2024. https://doi.org/10.32604/cmc.2024.046880.

Article  Google Scholar 

Komalasari R Secure and privacy-preserving federated learning with explainable artificial intelligence for smart healthcare systems. In: Federated Learning and Privacy-Preserving in Healthcare AI; 2024,  Hershey: IGI Global, pp. 288–313. Chap. 18. https://doi.org/10.4018/979-8-3693-1874-4.ch018

Wang L, Jayaraman B, Evans D, Gu Q Efficient privacy-preserving stochastic nonconvex optimization. In: Evans, R.J., Shpitser, I. (eds.) Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence. Proceedings of Machine Learning Research, 2023; PMLR 216; pp. 2203–2213. https://doi.org/10.5555/3625834.3626040

Yar M, Dahman AM, Mohammed AW, Vinh HT, Ryan A. Identification of pneumonia disease applying an intelligent computational framework based on deep learning and machine learning techniques. London: Hindawi; 2021.

Google Scholar 

Bonawitz K, Ivanov V, Kreuter B, Marcedone A, McMahan HB, Patel S, Ramage D, Segal A, Seth K, Practical secure aggregation for privacy-preserving machine learning. In: Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security. CCS ’17. New York: Association for Computing Machinery; pp. 1175–1191. (2017). https://doi.org/10.1145/3133956.3133982

Zhang C, Li S, Xia J, Wang W, Yan F, Liu Y, BatchCrypt: efficient homomorphic encryption for cross-silo federated learning. In: 2020 USENIX Annual Technical Conference (USENIX ATC 20), 2020:493–506. https://doi.org/10.5555/3489146.3489179

Madi A, Stan O, Mayoue A, Grivet-Sébert A, Gouy-Pailler C, Sirdey R, A secure federated learning framework using homomorphic encryption and verifiable computing. In: 2021 Reconciling Data Analytics, Automation, Privacy, and Security: A Big Data Challenge (RDAAPS), 2021:1–8 https://doi.org/10.1109/RDAAPS48126.2021.9452005 . IEEE

Xie H, Wang Y, Ding Y, Yang C, Zheng H, Qin B. Verifiable federated learning with privacy-preserving data aggregation for consumer electronics. IEEE Trans Consumer Electron. 2024;70(1):2696–707. https://doi.org/10.1109/TCE.2023.3323206.

Article  Google Scholar 

Brunetta C, Tsaloli G, Liang B, Banegas G, Mitrokotsa A, Non-interactive, secure verifiable aggregation for decentralized, privacy-preserving learning. In: Australasian Conference on Information Security and Privacy, New York: Springer; 2021:510–528.

Ma X, Zhang F, Chen X, Shen J. Privacy preserving multi-party computation delegation for deep learning in cloud computing. Inform Sci. 2018;459:103–16. https://doi.org/10.1016/j.ins.2018.05.005.

Article  Google Scholar 

Zhang X, Fu A, Wang H, Zhou C, Chen Z, A privacy-preserving and verifiable federated learning scheme. In: ICC 2020-2020 IEEE International Conference on Communications (ICC), 2020:1–6 . https://doi.org/10.1109/ICC40277.2020.9148628 . IEEE

Siddhartha M, Heart disease dataset (Comprehensive). https://doi.org/10.21227/dz4t-cm36