{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T13:38:04Z","timestamp":1781703484944,"version":"3.54.5"},"reference-count":39,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2022,11,18]],"date-time":"2022-11-18T00:00:00Z","timestamp":1668729600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Institute of Information and Communications Technology (NICT), JAPAN"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"<jats:p>Dynamic and smart Internet of Things (IoT) infrastructures allow the development of smart healthcare systems, which are equipped with mobile health and embedded healthcare sensors to enable a broad range of healthcare applications. These IoT applications provide access to the clients\u2019 health information. However, the rapid increase in the number of mobile devices and social networks has generated concerns regarding the secure sharing of a client\u2019s location. In this regard, federated learning (FL) is an emerging paradigm of decentralized machine learning that guarantees the training of a shared global model without compromising the data privacy of the client. To this end, we propose a K-anonymity-based secure hierarchical federated learning (SHFL) framework for smart healthcare systems. In the proposed hierarchical FL approach, a centralized server communicates hierarchically with multiple directly and indirectly connected devices. In particular, the proposed SHFL formulates the hierarchical clusters of location-based services to achieve distributed FL. In addition, the proposed SHFL utilizes the K-anonymity method to hide the location of the cluster devices. Finally, we evaluated the performance of the proposed SHFL by configuring different hierarchical networks with multiple model architectures and datasets. The experiments validated that the proposed SHFL provides adequate generalization to enable network scalability of accurate healthcare systems without compromising the data and location privacy.<\/jats:p>","DOI":"10.3390\/fi14110338","type":"journal-article","created":{"date-parts":[[2022,11,21]],"date-time":"2022-11-21T03:11:21Z","timestamp":1669000281000},"page":"338","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["SHFL: K-Anonymity-Based Secure Hierarchical Federated Learning Framework for Smart Healthcare Systems"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0036-1714","authenticated-orcid":false,"given":"Muhammad","family":"Asad","sequence":"first","affiliation":[{"name":"Graduate School of Information Science and Technology, Department of Creative Informatics, The University of Tokyo, Tokyo 113-8654, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9697-6766","authenticated-orcid":false,"given":"Muhammad","family":"Aslam","sequence":"additional","affiliation":[{"name":"School of Computing, Engineering, and Physical Sciences, University of the West of Scotland, Glasgow G72 0LH, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4751-8574","authenticated-orcid":false,"given":"Syeda Fizzah","family":"Jilani","sequence":"additional","affiliation":[{"name":"Department of Physics, Aberystwyth University, Aberystwyth SY23 3FL, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Saima","family":"Shaukat","sequence":"additional","affiliation":[{"name":"Graduate School of Information Science and Technology, Department of Creative Informatics, The University of Tokyo, Tokyo 113-8654, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8045-3939","authenticated-orcid":false,"given":"Manabu","family":"Tsukada","sequence":"additional","affiliation":[{"name":"Graduate School of Information Science and Technology, Department of Creative Informatics, The University of Tokyo, Tokyo 113-8654, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1537","DOI":"10.1109\/TII.2014.2300338","article-title":"Internet of things for enterprise systems of modern manufacturing","volume":"10","author":"Bi","year":"2014","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Chiuchisan, I., Chiuchisan, I., and Dimian, M. (2015, January 29\u201330). Internet of Things for e-Health: An approach to medical applications. Proceedings of the 2015 International Workshop on Computational Intelligence for Multimedia Understanding (IWCIM), Prague, Czech Republic.","DOI":"10.1109\/IWCIM.2015.7347091"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"101957","DOI":"10.1016\/j.scs.2019.101957","article-title":"Application and assessment of internet of things toward the sustainability of energy systems: Challenges and issues","volume":"53","author":"Khatua","year":"2020","journal-title":"Sustain. Cities Soc."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"52","DOI":"10.17485\/ijst\/2016\/v9i37\/102547","article-title":"A design characteristics of smart healthcare system as the IoT application","volume":"9","author":"Jeong","year":"2016","journal-title":"Indian J. Sci. Technol."},{"key":"ref_5","first-page":"307","article-title":"p A Lightweight and Robust User Authentication Protocol with User Anonymity for IoT-Based Healthcare","volume":"131","author":"Chen","year":"2022","journal-title":"CMES-Comput. Model. Eng. Sci."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"375","DOI":"10.1016\/j.future.2017.10.045","article-title":"A new architecture of Internet of Things and big data ecosystem for secured smart healthcare monitoring and alerting system","volume":"82","author":"Manogaran","year":"2018","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1109\/MCOM.2017.1600374CM","article-title":"Federated internet of things and cloud computing pervasive patient health monitoring system","volume":"55","author":"Abawajy","year":"2017","journal-title":"IEEE Commun. Mag."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Patel, W., Pandya, S., and Mistry, V. (2016, January 23\u201325). i-MsRTRM: Developing an IoT based Intelligent Medicare system for Real-Time Remote Health monitoring. Proceedings of the 2016 8th International Conference on Computational Intelligence and Communication Networks (CICN), Tehri, India.","DOI":"10.1109\/CICN.2016.132"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2818","DOI":"10.1109\/TMC.2020.3045266","article-title":"Fedhome: Cloud-edge based personalized federated learning for in-home health monitoring","volume":"21","author":"Wu","year":"2020","journal-title":"IEEE Trans. Mob. Comput."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"6134","DOI":"10.1109\/TII.2020.2984366","article-title":"A lightweight privacy-aware iot-based metering scheme for smart industrial ecosystems","volume":"17","author":"Ali","year":"2021","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"972","DOI":"10.1109\/LWC.2022.3151873","article-title":"FBI: A federated learning-based blockchain-embedded data accumulation scheme using drones for Internet of Things","volume":"11","author":"Islam","year":"2022","journal-title":"IEEE Wirel. Commun. Lett."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Gribbestad, M., Hassan, M.U., A Hameed, I., and Sundli, K. (2021). Health Monitoring of Air Compressors Using Reconstruction-Based Deep Learning for Anomaly Detection with Increased Transparency. Entropy, 23.","DOI":"10.3390\/e23010083"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Malik, S., Rouf, R., Mazur, K., and Kontsos, A. (2020). The Industry Internet of Things (IIoT) as a Methodology for Autonomous Diagnostics in Aerospace Structural Health Monitoring. Aerospace, 7.","DOI":"10.3390\/aerospace7050064"},{"key":"ref_14","first-page":"69","article-title":"CNN-Based Deep Architecture for Health Monitoring of Civil and Industrial Structures Using UAVs","volume":"42","author":"Harweg","year":"2019","journal-title":"Multidiscip. Digit. Publ. Inst. Proc."},{"key":"ref_15","unstructured":"Zhu, X., Li, H., and Yu, Y. Blockchain-based privacy preserving deep learning. Proceedings of the International Conference on Information Security and Cryptology."},{"key":"ref_16","first-page":"1","article-title":"Federated learning","volume":"13","author":"Yang","year":"2019","journal-title":"Synth. Lect. Artif. Intell. Mach. Learn."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"758","DOI":"10.1007\/s00103-019-02955-5","article-title":"Current challenges in the assessment of ethical proposals-aspects of digitalization and personalization in the healthcare system","volume":"62","author":"Rauch","year":"2019","journal-title":"Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1109\/TPDS.2020.3009406","article-title":"Self-balancing federated learning with global imbalanced data in mobile systems","volume":"32","author":"Duan","year":"2020","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Sun, L., Qian, J., Chen, X., and Yu, P.S. (2020). Ldp-fl: Practical private aggregation in federated learning with local differential privacy. arXiv.","DOI":"10.24963\/ijcai.2021\/217"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"3241","DOI":"10.1109\/TWC.2020.2971981","article-title":"A crowdsourcing framework for on-device federated learning","volume":"19","author":"Pandey","year":"2021","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Thrun, M.C., and Ultsch, A. (2020). Using Projection-Based Clustering to Find Distance-and Density-Based Clusters in High-Dimensional Data. J. Classif., 1\u201333.","DOI":"10.1007\/s00357-020-09373-2"},{"key":"ref_22","unstructured":"Berke, A., Bakker, M., Vepakomma, P., Raskar, R., Larson, K., and Pentland, A. (2020). Assessing disease exposure risk with location histories and protecting privacy: A cryptographic approach in response to a global pandemic. arXiv."},{"key":"ref_23","unstructured":"Li, X., Huang, K., Yang, W., Wang, S., and Zhang, Z. (2019). On the convergence of fedavg on non-iid data. arXiv."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2031","DOI":"10.1109\/COMST.2020.2986024","article-title":"Federated learning in mobile edge networks: A comprehensive survey","volume":"22","author":"Lim","year":"2020","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_25","unstructured":"Caldas, S., Kone\u010dny, J., McMahan, H.B., and Talwalkar, A. (2018). Expanding the reach of federated learning by reducing client resource requirements. arXiv."},{"key":"ref_26","unstructured":"Luping, W., Wei, W., and Bo, L. (2019, January 7\u201310). CMFL: Mitigating communication overhead for federated learning. Proceedings of the 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), Dallas, TX, USA."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Asad, M., Moustafa, A., and Ito, T. (2020). FedOpt: Towards communication efficiency and privacy preservation in federated learning. Appl. Sci., 10.","DOI":"10.3390\/app10082864"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"3710","DOI":"10.1109\/TNNLS.2020.3015958","article-title":"Clustered federated learning: Model-agnostic distributed multitask optimization under privacy constraints","volume":"32","author":"Sattler","year":"2020","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1109\/MCOM.001.2000397","article-title":"Wireless communications for collaborative federated learning","volume":"58","author":"Chen","year":"2020","journal-title":"IEEE Commun. Mag."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Liu, L., Zhang, J., Song, S., and Letaief, K.B. (2020, January 7\u201311). Client-edge-cloud hierarchical federated learning. Proceedings of the ICC 2020-2020 IEEE International Conference on Communications (ICC), Dublin, Ireland.","DOI":"10.1109\/ICC40277.2020.9148862"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Zhang, J., Chen, B., Yu, S., and Deng, H. (2019, January 9\u201313). PEFL: A privacy-enhanced federated learning scheme for big data analytics. Proceedings of the 2019 IEEE Global Communications Conference (GLOBECOM), Waikoloa, HI, USA.","DOI":"10.1109\/GLOBECOM38437.2019.9014272"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.ins.2020.02.037","article-title":"A training-integrity privacy-preserving federated learning scheme with trusted execution environment","volume":"522","author":"Chen","year":"2020","journal-title":"Inf. Sci."},{"key":"ref_33","unstructured":"Bonawitz, K., Eichner, H., Grieskamp, W., Huba, D., Ingerman, A., Ivanov, V., Kiddon, C., Kone\u010dn\u1ef3, J., Mazzocchi, S., and McMahan, H.B. (2019). Towards federated learning at scale: System design. arXiv."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"6532","DOI":"10.1109\/TII.2019.2945367","article-title":"Efficient and privacy-enhanced federated learning for industrial artificial intelligence","volume":"16","author":"Hao","year":"2019","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-based learning applied to document recognition","volume":"86","author":"LeCun","year":"1998","journal-title":"Proc. IEEE"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016). Identity mappings in deep residual networks. European Conference on Computer Vision Proceedings of the Identity Mappings in Deep Residual Networks, Springer.","DOI":"10.1007\/978-3-319-46493-0_38"},{"key":"ref_37","unstructured":"Zhao, J., Zhang, Y., He, X., and Xie, P. (2020). Covid-ct-dataset: A ct scan dataset about covid-19. arXiv."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Hao, M., Li, H., Xu, G., Liu, S., and Yang, H. (2019, January 20\u201324). Towards efficient and privacy-preserving federated deep learning. Proceedings of the ICC 2019-2019 IEEE International Conference on Communications (ICC), Shanghai, China.","DOI":"10.1109\/ICC.2019.8761267"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Liu, Y., Ma, Z., Liu, X., Ma, S., Nepal, S., and Deng, R. (2019). Boosting privately: Privacy-preserving federated extreme boosting for mobile crowdsensing. arXiv.","DOI":"10.1109\/ICDCS47774.2020.00017"}],"container-title":["Future Internet"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-5903\/14\/11\/338\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:21:35Z","timestamp":1760145695000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-5903\/14\/11\/338"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,18]]},"references-count":39,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2022,11]]}},"alternative-id":["fi14110338"],"URL":"https:\/\/doi.org\/10.3390\/fi14110338","relation":{},"ISSN":["1999-5903"],"issn-type":[{"value":"1999-5903","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,11,18]]}}}