The DOI matches its Crossref record; 43 of the 43 references it cites were found in a registry or an index.
RETRACTED: FL-PMI: Federated Learning-Based Person Movement Identification through Wearable Devices in Smart Healthcare Systems
Open accessAbstract
Recent technological developments, such as the Internet of Things (IoT), artificial intelligence, edge, and cloud computing, have paved the way in transforming traditional healthcare systems into smart healthcare (SHC) systems. SHC escalates healthcare management with increased efficiency, convenience, and personalization, via use of wearable devices and connectivity, to access information with rapid responses. Wearable devices are equipped with multiple sensors to identify a person’s movements. The unlabeled data acquired from these sensors are directly trained in the cloud servers, which require vast memory and high computational costs. To overcome this limitation in SHC, we propose a federated learning-based person movement identification (FL-PMI). The deep reinforcement learning (DRL) framework is leveraged in FL-PMI for auto-labeling the unlabeled data. The data are then trained using federated learning (FL), in which the edge servers allow the parameters alone to pass on the cloud, rather than passing vast amounts of sensor data. Finally, the bidirectional long short-term memory (BiLSTM) in FL-PMI classifies the data for various processes associated with the SHC. The simulation results proved the efficiency of FL-PMI, with 99.67% accuracy scores, minimized memory usage and computational costs, and reduced transmission data by 36.73%.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
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?
In the citing paper
“FL is a paradigm that aims to collaborate on data management and privacy issues by using evaluation metrics without transferring samples [47–49].”
From A comprehensive survey on federated learning techniques for healthcare informatics (2023) · cites this paper as [49]
In the citing paper
“AI and its applications in healthcare have recently made considerable strides [19].”
From Federated learning-based AI approaches in smart healthcare: Concepts, taxonomies, challenges and open issues (2023) · cites this paper as [19]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
A survey on security and privacy of federated learning
Mothukuri, V., Parizi, R. M., Pouriyeh, S., Huang, Y., Dehghantanha, A., & Srivastava, G. (2021) · Future Generation Computer Systems · doi:10.1016/j.future.2020.10.007 · Cited by 1,480
Wearable Sensors for Remote Health Monitoring
Majumder, S., Mondal, T., & Deen, M. (2017) · Sensors · doi:10.3390/s17010130 · Cited by 1,375
FedHealth: A Federated Transfer Learning Framework for Wearable Healthcare
Chen, Y., Qin, X., Wang, J., Yu, C., & Gao, W. (2020) · IEEE Intelligent Systems · doi:10.1109/mis.2020.2988604 · Cited by 1,005
UniMiB SHAR: A Dataset for Human Activity Recognition Using Acceleration Data from Smartphones
Micucci, D., Mobilio, M., & Napoletano, P. (2017) · doi:10.20944/preprints201706.0033.v1 · Cited by 596 · cited 4×
“For this evaluation, we considered the most popular and widely used UniMiB SHAR [18] dataset.”
MethodInfluentialUniMiB SHAR: A Dataset for Human Activity Recognition Using Acceleration Data from Smartphones
Micucci, D., Mobilio, M., & Napoletano, P. (2017) · Applied Sciences · doi:10.3390/app7101101 · Cited by 596
IoT-Based Applications in Healthcare Devices
Pradhan, B. K., Bhattacharyya, S., & Pal, K. (2021) · Journal of Healthcare Engineering · doi:10.1155/2021/6632599 · Cited by 481
On-body localization of wearable devices: An investigation of position-aware activity recognition
Sztyler, T., & Stuckenschmidt, H. (2016) · 2016 IEEE International Conference on Pervasive Computing and Communications (PerCom) · doi:10.1109/percom.2016.7456521 · Cited by 369
A Review on Human Activity Recognition Using Vision-Based Method
Zhang, S., Wei, Z., Nie, J., Huang, L., Wang, S., & Li, Z. (2017) · Journal of Healthcare Engineering · doi:10.1155/2017/3090343 · Cited by 345
Federated Learning for Cybersecurity: Concepts, Challenges, and Future Directions
Alazab, M., Ramu, S. P., Parimala, M., Maddikunta, P. K. R., Gadekallu, T. R., & Pham, Q. (2021) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2021.3119038 · Cited by 326
Self-citationComparison of Feature Learning Methods for Human Activity Recognition Using Wearable Sensors
Li, F., Shirahama, K., Nisar, M., Köping, L., & Grzegorzek, M. (2018) · Sensors · doi:10.3390/s18020679 · Cited by 302
Research works printed but not linked: 2
- Wu, M., and Luo, J. (2019). Wearable technology applications in healthcare: A literature review. Online J. Nurs. Inform., 23.
- Gadekallu, T.R., Pham, Q.V., Huynh-The, T., Bhattacharya, S., Maddikunta, P.K.R., and Liyanage, M. (2021). Federated Learning for Big Data: A Survey on Opportunities, Applications, and Future Directions. arXiv.
Cited by
Sorted by citations
Rahman, A., Hossain, M. S., Muhammad, G., Kundu, D., Debnath, T., Rahman, M., Khan, M. S. I., Tiwari, P., & Band, S. S. (2023) · Cluster Computing · doi:10.1007/s10586-022-03658-4 · Cited by 399
Teo, Z. L., Jin, L., Liu, N., Li, S., Miao, D., Zhang, X., Ng, W. Y., Tan, T. F., Lee, D. M., Chua, K. J., et al. (2024) · Cell Reports Medicine · doi:10.1016/j.xcrm.2024.101419 · Cited by 255
“14 In the original FL framework, a centralized server broadcasts initial weights of the global model parameters to a set of selected participating site.”
MethodEdge Computing in Healthcare: Innovations, Opportunities, and Challenges
Rancea, A., Anghel, I. M., & Cioara, T. (2024) · Future Internet · doi:10.3390/fi16090329 · Cited by 238
Review on security of federated learning and its application in healthcare
Li, H., Li, C., Wang, J., Yang, A., Ma, Z., Zhang, Z., & Hua, D. (2023) · Future Generation Computer Systems · doi:10.1016/j.future.2023.02.021 · Cited by 202
A comprehensive survey on federated learning techniques for healthcare informatics
Dasaradharami Reddy, K., & Gadekallu, T. R. (2023) · Computational Intelligence and Neuroscience · doi:10.1155/2023/8393990 · Cited by 126
Self-citationJouini, O., Sethom, K., Namoun, A., Aljohani, N. F., Alanazi, M. H., & Alanazi, M. N. (2024) · Technologies · doi:10.3390/technologies12060081 · Cited by 122
FL-FD: Federated learning-based fall detection with multimodal data fusion
Qi, P., Chiaro, D., & Piccialli, F. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.101890 · Cited by 125
Ullah, F., Nadeem, M., Abrar, M. Y. B. T., Amin, F., Salam, A., & Khan, S. (2023) · Mathematics · doi:10.3390/math11194189 · Cited by 114
Leveraging IoT-Aware Technologies and AI Techniques for Real-Time Critical Healthcare Applications
Shumba, A. T., Montanaro, T., Sergi, I., Fachechi, L., Vittorio, M. D., & Patrono, L. (2022) · Sensors · doi:10.3390/s22197675 · Cited by 97
Mittal, S., Bansal, A., Gupta, D., Juneja, S., Turabieh, H., Elarabawy, M. M., Sharma, A. K., & Bitsue, Z. K. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/7016554 · Cited by 93
Federated Learning for the Internet-of-Medical-Things: A Survey
Prasad, V. K., Bhattacharya, P., Maru, D., Tanwar, S., Verma, A., Singh, A., Tiwari, A. K., Sharma, R., Alkhayyat, A. H., Țurcanu, F.-E., et al. (2022) · Mathematics · doi:10.3390/math11010151 · Cited by 87
(Benefits of Internet of Things (IoT) Applications in Health care - An Overview)
Salama, R., Al-Turjman, F. M., Chaudhary, P. M., & Yadav, S. P. (2023) · International Conference on Computational Intelligence, Communication Technology and Networking (CICTN) · doi:10.1109/cictn57981.2023.10141452 · Cited by 78
Shakeel, T., Habib, S., Boulila, W., Koubâa, A., Javed, A. R., Rizwan, M., Gadekallu, T. R., & Sufiyan, M. (2022) · Complex & Intelligent Systems · doi:10.1007/s40747-022-00767-w · Cited by 84
Self-citationGrzesik, P., & Mrozek, D. (2024) · Electronics · doi:10.3390/electronics13030640 · Cited by 75
Deep Unsupervised Domain Adaptation with Time Series Sensor Data: A Survey
Shi, Y., Ying, X., & Yang, J. (2022) · Sensors · doi:10.3390/s22155507 · Cited by 85
Federated Learning on Multimodal Data: A Comprehensive Survey
Lin, Y., Gao, Y., Gong, M., Zhang, S., Zhang, Y., & Li, Z. (2023) · Machine Intelligence Research · doi:10.1007/s11633-022-1398-0 · Cited by 73
Blockchain and cloud computing-based secure electronic healthcare records storage and sharing
Amanat, A., Rizwan, M., Maple, C. R., Zikria, Y. B., Almadhor, A. S., & Kim, S. W. (2022) · Frontiers in Public Health · doi:10.3389/fpubh.2022.938707 · Cited by 85
A Sequential Machine Learning-cum-Attention Mechanism for Effective Segmentation of Brain Tumor
Ali, T. M., Nawaz, A., Rehman, A. U., Ahmad, R. Z., Javed, A. R., Gadekallu, T. R., Chen, C., & Wu, C.-M. (2022) · Frontiers in Oncology · doi:10.3389/fonc.2022.873268 · Cited by 60
Self-citationSurvey on Sensors and Smart Devices for IoT Enabled Intelligent Healthcare System
Chopade, S., Gupta, H. P., & Dutta, T. (2023) · Wireless Personal Communications · doi:10.1007/s11277-023-10528-8 · Cited by 56
A Comprehensive Survey on Privacy-Preserving Techniques in Federated Recommendation Systems
Asad, M., Shaukat, S., Javanmardi, E., Nakazato, J., & Tsukada, M. (2023) · Applied Sciences · doi:10.3390/app13106201 · Cited by 52
Federated learning design and functional models: survey
Ayeelyan, J., Utomo, S., Rouniyar, A., Hsu, H.-C., & Hsiung, P. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10969-y · Cited by 56
Madathil, N. T., Dankar, F. K., Gergely, M., Belkacem, A. N., & Alrabaee, S. (2025) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2025.06.009 · Cited by 58
Genetic Clustered Federated Learning for COVID-19 Detection
Kandati, D. R., & Gadekallu, T. R. (2022) · Electronics · doi:10.3390/electronics11172714 · Cited by 45
Self-citationAn IoT-Based Framework for Personalized Health Assessment and Recommendations Using Machine Learning
Jagatheesaperumal, S. K., Rajkumar, S., Suresh, J. V., Gumaei, A., Alhakbani, N., Uddin, M. Z., & Hassan, M. M. (2023) · Mathematics · doi:10.3390/math11122758 · Cited by 46
Prediction of Dental Implants Using Machine Learning Algorithms
Alharbi, M., & Almutiq, M. (2022) · Journal of Healthcare Engineering · doi:10.1155/2022/7307675 · Cited by 46
Privacy-Preserving Federated Learning in Healthcare
Moon, S., & Lee, W. H. (2023) · International Conference on Electronics, Information, and Communication (ICEIC) · doi:10.1109/iceic57457.2023.10049966 · Cited by 42
RPIFL: Reliable and Privacy-Preserving Federated Learning for the Internet of Things
Wang, R., Lai, J., Li, X., He, D., & Khan, M. K. (2023) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2023.103768 · Cited by 42
Muazu, T., Mao, Y., Muhammad, A. U., bin Ibrahim, M., Kumshe, U. M. M., & Samuel, O. (2024) · Computer Communications · doi:10.1016/j.comcom.2024.01.006 · Cited by 42
Detecting Electrocardiogram Arrhythmia Empowered With Weighted Federated Learning
Asif, R. N., Ditta, A., Alquhayz, H., Abbas, S., Khan, M. A., Ghazal, T. M., & Lee, S.-W. (2023) · IEEE Access · doi:10.1109/access.2023.3347610 · Cited by 37
Pawar, S. D., Sharma, K. K., Sapate, S. G., Yadav, G. Y., Alroobaea, R., Alzahrani, S. M., & Hedabou, M. (2022) · Frontiers in Public Health · doi:10.3389/fpubh.2022.885212 · Cited by 33
Fault Prediction Recommender Model for IoT Enabled Sensors Based Workplace
Uppal, M., Gupta, D., Mahmoud, A., Elmagzoub, M. A., Sulaiman, A. A., Reshan, M. S. A., Shaikh, A., & Juneja, S. (2023) · Sustainability · doi:10.3390/su15021060 · Cited by 33
Federated Learning for Human Activity Recognition: Overview, Advances, and Challenges
Aouedi, O., Sacco, A., Khan, L. U., Nguyen, D. C., & Guizani, M. (2024) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2024.3484228 · Cited by 42
Survey of Medical Applications of Federated Learning
Choi, G., Cha, W. C., Lee, S. U., & Shin, S.-Y. (2024) · Healthcare Informatics Research · doi:10.4258/hir.2024.30.1.3 · Cited by 33
Guembe, B., Misra, S., & Azeta, A. A. (2024) · Journal of Parallel and Distributed Computing · doi:10.1016/j.jpdc.2024.104964 · Cited by 33
A review of federated learning: taxonomy, privacy and future directions
Ratnayake, H., Chen, L., & Ding, X. (2023) · Journal of Intelligent Information Systems · doi:10.1007/s10844-023-00797-x · Cited by 32
Prathiba, S. B., Govindarajan, Y., Ganesan, V. P. A., Ramachandran, A., Selvaraj, A. K., Bashir, A. K., & Gadekallu, T. R. (2024) · IEEE Access · doi:10.1109/access.2024.3401039 · Cited by 26
Self-citationGupta, M., Kumar, M., & Dhir, R. (2024) · Computer Science Review · doi:10.1016/j.cosrev.2024.100685 · Cited by 27
Zhou, F., Hu, S., Du, X., Xiaoli, W., & Wu, J. (2024) · Future Internet · doi:10.3390/fi16030075 · Cited by 24
Explainable Deep Attention Active Learning for Sentimental Analytics of Mental Disorder
Ahmed, U., Jhaveri, R. H., Srivastava, G., & Lin, J. C. (2022) · ACM Transactions on Asian and Low-Resource Language Information Processing · doi:10.1145/3551890 · Cited by 26
Gadekallu, T. R., Alazab, M., Hemanth, D. J., & Wang, W. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2023.3234604 · Cited by 24
Self-citationAdvancing indoor positioning systems: innovations, challenges, and applications in mobile robotics
Deshmukh, R. A., Hasamnis, M. A., Kulkarni, M. B., & Bhaiyya, M. L. (2025) · Robotica · doi:10.1017/s0263574725101872 · Cited by 23
Multimodal Federated Learning in Healthcare: A Review
Thrasher, J., Devkota, A., Siwakoti, P., Chivukula, R., Poudel, P., Hu, C., Tafti, A. P., Bhattarai, B., & Gyawali, P. (2025) · Journal of Healthcare Informatics Research · doi:10.1007/s41666-025-00226-4 · Cited by 46
Chai, Y., Liu, H., Zhu, H., Pan, Y., Zhou, A., Liu, H., Liu, J., & Qian, Y. (2024) · Information & Management · doi:10.1016/j.im.2024.103922 · Cited by 24
Federated learning for rare disease detection: a survey
Wang, J., & Ma, F. (2023) · Rare Disease and Orphan Drugs Journal · doi:10.20517/rdodj.2023.16 · Cited by 21
Selvaraj, A. K., Prathiba, S. B., Deepak Kumar, A., Dhanalakshmi, R., Gadekallu, T. R., & Srivastava, G. (2024) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2024.3460469 · Cited by 21
Self-citationA Comprehensive Survey on Federated Learning Applications in Computational Mental Healthcare
Vajrobol, V., Singh, S., Saxena, G. J., Pundir, A., Gaurav, A., Bansal, S., Attar, R. W., Rahman, M., & Gupta, B. B. (2024) · Computer Modeling in Engineering & Sciences · doi:10.32604/cmes.2024.056500 · Cited by 26
The Role of Machine Learning in IoT: A Survey
Arikumar, K. S., Prathiba, S. B., Moorthy, R. S., Tamilarasi, K., Chalapathi, M. M. V., & Kumar, A. (2022) · International Conference on Smart Electronics and Communication (ICOSEC) · doi:10.1109/icosec54921.2022.9952042 · Cited by 21
Self-citationManoharan, H., Selvarajan, S., Yafoz, A., Alterazi, H. A., Uddin, M., Chen, C., & Wu, C.-M. (2022) · Frontiers in Public Health · doi:10.3389/fpubh.2022.909628 · Cited by 19
Novel IoT framework for event processing in healthcare applications
Shaikh, N., Kasat, K., Godi, R. K., Krishna, V. R., Chauhan, D. K., & Kharade, J. (2023) · Measurement Sensors · doi:10.1016/j.measen.2023.100733 · Cited by 19
Malik, H., & Anees, T. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-023-18065-z · Cited by 20
The Internet of Things under Federated Learning: A Review of the Latest Advances and Applications
Liu, Z., Yang, X., Li, M., Wang, J., & Lv, Z. (2025) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2024.058926 · Cited by 23
Securing the edge: privacy-preserving federated learning for insider threats in IoT networks
Kamatchi, K., & Uma, E. (2024) · The Journal of Supercomputing · doi:10.1007/s11227-024-06752-z · Cited by 17
Huang, Z., Wang, W., Jia, Z., & Wang, Z. (2024) · Journal of Computer and Communications · doi:10.4236/jcc.2024.1212012 · Cited by 17
Wassan, S., Liudajun, Han, Y., Dongyan, H., & Fei, P. (2025) · Digital Health · doi:10.1177/20552076251358531 · Cited by 22
Diagnosis of Chronic Ischemic Heart Disease Using Machine Learning Techniques
Shehzadi, S., Hassan, M. A., Rizwan, M., Kryvinska, N., & Karovič, V. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/3823350 · Cited by 16
Selvaraj, A. K., Prathiba, S. B., Moorthy, R. S., Srivastava, G., & Gadekallu, T. R. (2023) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2023.3293053 · Cited by 15
Self-citationSravan, P., Saranya, S., Deepika, N. M., Nijhawan, G., Alawadi, A. H., & Suresh Kumar, M., V (2023) · International Conference on Artificial Intelligence for Innovations in Healthcare Industries (ICAIIHI) · doi:10.1109/icaiihi57871.2023.10489632 · Cited by 14
Imbesi, S., Corzani, M., Lopane, G., Mincolelli, G., & Chiari, L. (2022) · Sensors · doi:10.3390/s22218095 · Cited by 14
Nemane, S. G., Sarad, V. M., Tulaskar, D. P., Marode, T. P., Bhangdiya, V. K., Agrawal, L., Avthankar, A., Kondylakis, H., Kulkarni, M. B., Bhaiyya, M. L., et al. (2026) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-026-10629-0 · Cited by 13
Age-appropriate design of smart senior care product APP interface based on deep learning
Chen, S. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e28567 · Cited by 14
Huang, Y., Zhou, Y., Zhao, H., Riedel, T., & Beigl, M. (2024) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn60899.2024.10650060 · Cited by 12
A Cluster-Driven Adaptive Training Approach for Federated Learning
Jeong, Y., & Kim, T. (2022) · Sensors · doi:10.3390/s22187061 · Cited by 12
Fuzzy-MAC: An FIS based MAC protocol for a multi-constrained traffic in wireless body area networks
Ray, K., Pal, V., Singal, G., & Moulik, S. (2022) · Computer Communications · doi:10.1016/j.comcom.2022.09.013 · Cited by 12
Kanauzia, R., Singh, M., Gulati, S., Singh, B. M., Arora, N., & Gola, K. K. (2026) · Knowledge and Information Systems · doi:10.1007/s10115-025-02673-2 · Cited by 16
Social Networking Security during COVID‐19: A Systematic Literature Review
Abid, R., Rizwan, M., Veselý, P., Basharat, A., Tariq, U., & Javed, A. R. (2022) · Wireless Communications and Mobile Computing · doi:10.1155/2022/2975033 · Cited by 13
Probabilistic Framework Allocation on Underwater Vehicular Systems Using Hydrophone Sensor Networks
Kshirsagar, P. R., Manoharan, H., Selvarajan, S., Alshareef, A. M., Singh, D., & Lee, H.-N. (2022) · Water · doi:10.3390/w14081292 · Cited by 11
Federated Markov Logic Network for indoor activity recognition in Internet of Things
Zhang, C., Ren, X., Zhu, T., Zhou, F., Liu, H., Lu, Q., & Ning, H. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.109553 · Cited by 12
Panda, S., Dubey, R., Jena, B., Pareek, V., Tsai, L.-W., & Saxena, S. (2025) · Applied Soft Computing · doi:10.1016/j.asoc.2025.112747 · Cited by 11
Yuan, J., & Chen, Y. (2025) · Computer Networks · doi:10.1016/j.comnet.2025.111139 · Cited by 12
Rani, S. S., Gopal, D., Vignesh, S., & Ramesh, P. S. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.108362 · Cited by 10
Developments in Artificial Intelligence for Ophthalmology: Federated Learning
Teo, Z. L., Lee, A., Campbell, P., Chan, R. V. P., & Ting, D. S. W. (2022) · Asia-Pacific Journal of Ophthalmology · doi:10.1097/apo.0000000000000582 · Cited by 13
Towards cost-effective and resource-aware aggregation at Edge for Federated Learning
Khan, A., Li, Y., Wang, X., Haroon, S., Ali, H., Cheng, Y., Butt, A. R., & Anwar, A. (2023) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata59044.2023.10386691 · Cited by 10
Healthcare federated learning: a survey of applications and frameworks
Pais, V., Rao, S., & Muniyal, B. (2025) · International Journal of Computers and Applications · doi:10.1080/1206212x.2025.2496913 · Cited by 9
Rajagopal, M. P., & Natesan, G. (2025) · Transactions on Emerging Telecommunications Technologies · doi:10.1002/ett.70187 · Cited by 8
Federated Transfer Learning for Vision-Based Fall Detection
Mistry, D., Tonmoy, M. R., Anower, M. S., & Hasan, A. S. M. T. (2024) · Lecture notes in networks and systems · doi:10.1007/978-981-99-8937-9_64 · Cited by 7
Moorthy, R. S., Selvaraj, A. K., Prathiba, S. B., Yenduri, G., Mohanty, S. N., & Ramesh, J. V. N. (2024) · IEEE Access · doi:10.1109/access.2024.3417822 · Cited by 7
Self-citationPSA-FL-CDM: A Novel Federated Learning-Based Consensus Model for Post-Stroke Assessment
Razfar, N., Kashef, R. F., & Mohammadi, F. A. (2024) · Sensors · doi:10.3390/s24165095 · Cited by 8
Chen, H. H., & Yue, X. (2024) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2024.1452019 · Cited by 7
Zhang, C., Zhu, T., Wu, H., & Ning, H. (2024) · Cluster Computing · doi:10.1007/s10586-024-04289-7 · Cited by 7
Noor, M. A., Hassan, S. B., Alam, M. S. B., Lameesa, A., & Siddique, M. A. R. (2025) · Array · doi:10.1016/j.array.2025.100500 · Cited by 6
Novel Architecture Integrating XAI with Blockchain and IoT Devices for Healthcare
Murthy, C. V. N. U. B., & Shri, M. L. (2024) · International Conference on Emerging Trends in Information Technology and Engineering (ICETITE) · doi:10.1109/ic-etite58242.2024.10493418 · Cited by 6
Heterogeneous Federated Learning for Non-IID Smartwatch Data Classification
Syu, J.-H., & Lin, J. C. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3410393 · Cited by 6
Enhancing the Security of WPA2/PSK Authentication Protocol in Wi-Fi Networks
Arikumar, K. S., Kumar, A., Prathiba, S. B., Tamilarasi, K., Moorthy, R. S., & Iqbal, M. M. (2022) · Procedia Computer Science · doi:10.1016/j.procs.2022.12.043 · Cited by 6
Self-citationA Survey of Advances in Multimodal Federated Learning with Applications
Barry, G., Konyar, E., Harvill, B., & Johnstone, C. (2024) · Springer optimization and its applications · doi:10.1007/978-3-031-53092-0_15 · Cited by 5
A Privacy Preserving Federated Learning System for IoT Devices Using Blockchain and Optimization
Han, Y. (2024) · Journal of Computer and Communications · doi:10.4236/jcc.2024.129005 · Cited by 5
Diabetes classification using MapReduce-based capsule network
Arun, G. K., & Marimuthu, C. N. (2023) · Automatika · doi:10.1080/00051144.2023.2284031 · Cited by 5
Ajao, A., Jonathan, O., & Adetiba, E. (2024) · International Conference on Science, Engineering and Business for Driving Sustainable Development Goals (SEB4SDG) · doi:10.1109/seb4sdg60871.2024.10629812 · Cited by 5
Early Detection of Cardiovascular Disease with Different Machine Learning Approaches
Singh, E., Singh, V., Rai, A., Christopher, I., Mishra, R. K., & Arikumar, K. S. (2024) · EAI Endorsed Transactions on Internet of Things · doi:10.4108/eetiot.5389 · Cited by 4
Self-citationNarwaria, M., & Jaiswal, S. (2024) · Cluster Computing · doi:10.1007/s10586-024-04466-8 · Cited by 4
Németh, G. D., Lozano, M. A., Quadrianto, N., & Oliver, N. (2025) · IEEE Access · doi:10.1109/access.2025.3546478 · Cited by 4
Alamer, F. A., AlQwaizani, E., & Althagafy, K. (2023) · Carbon letters · doi:10.1007/s42823-023-00619-8 · Cited by 4
IoMT and blockchain oriented smart healthcare monitoring system
Ali, M. A., & Dhanaraj, R. K. (2024) · AIP conference proceedings · doi:10.1063/5.0184364 · Cited by 4
Legal implications of federated learning integration in digital healthcare systems
Imoize, A. L., Obaidat, M. S., & Song, H. H. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-13897-3.00014-x · Cited by 3
Etumusei, J., Carracedo, J. M., & McClean, S. I. (2025) · SN Computer Science · doi:10.1007/s42979-025-03895-5 · Cited by 3
Advancements in Federated Learning for Health Applications: A Concise Survey
Stamatis, V., Grammatikis, P. I. R., Sarigiannidis, A. G., Pitropakis, N., Λάγκας, Θ., Argyriou, V., Markakis, E., & Sarigiannidis, P. G. (2024) · International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT) · doi:10.1109/dcoss-iot61029.2024.00080 · Cited by 4
A Federated Learning-Based Framework for Accurately Identifying Human Activity in the Environment
Al-Blihed, N. S., & Ibrahim, D. M. (2025) · IEEE Access · doi:10.1109/access.2025.3583556 · Cited by 5
A hybrid DL with the Internet of Things to monitor human activities using wearable sensors
Jeba Sheela, A., Gowthami, M. D., Kumar, V. S. R., Prabu, C., V, & Queen Mary Vidya, M. (2022) · Measurement Sensors · doi:10.1016/j.measen.2022.100496 · Cited by 3
FederatedMesh: Collaborative Federated Learning for Medical Data Sharing in Mesh Networks
Shkurti, L., Selimi, M., & Besimi, A. (2024) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-031-54531-3_9 · Cited by 3
Federated Learning for Secure Healthcare Image Analysis in the Cloud
Varshney, N., Madan, P., Shrivastava, A., Kumar, C. P., Rao, A. L. N., & Khan, A. K. (2023) · International Conference on Artificial Intelligence for Innovations in Healthcare Industries (ICAIIHI) · doi:10.1109/icaiihi57871.2023.10489066 · Cited by 3
Human Knowledge-based Compressed Federated Learning Model for Wind Turbine Blade Icing Detection
Zhang, D., Tian, W., Yin, Y., Liu, X., Cheng, X., & Shi, F. (2022) · International Conference on High Performance Big Data and Intelligent Systems (HPBD&IS) · doi:10.1109/hdis56859.2022.9991642 · Cited by 3
Metadata
- Authors
- Arikumar, K., Prathiba, S., Alazab, M., Gadekallu, T., Pandya, S., Khan, J., & Moorthy, R.
- Affiliations
- Department of Computer Science and Engineering, St. Joseph’s Institute of Technology, Chennai 600119, India
- Anna University, Chennai (IN)
- Charles Darwin University (AU)
- Vellore Institute of Technology University (IN)
- Symbiosis International University (IN)
- King Saud University (SA)
- Sri Ramachandra Institute of Higher Education and Research (IN)
- Corresponding author
- Gadekallu, Thippa
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2022-02-11
- Volume
- 22
- Issue
- 4
- Pages
- 1377
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC8962969
- OpenAlex
- W4211249696
- Cited by
- 168
- References
- 42
- Citations by year
- 2026: 24 · 2025: 43 · 2024: 45 · 2023: 29 · 2022: 27
- FWCI
- 11.85
- Type
- journal article
- Language
- en
- Status
- Retracted
- Update notices
- retraction (2026-09-21)
- Open access
- gold
- doi.org
- Licence
- Locations
- Sensors (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DiVA (Linnaeus University) (submittedVersion, other-oa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- King Saud University (RSP-2021/360, RSP-2021)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Keywords
- federated learning, wearable devices, smart healthcare, deep reinforcement learning, auto-labeling, unlabeled data, edge computing, bidirectional LSTM, Internet of Things, memory and computational cost reduction
- MeSH terms
- Cloud Computing, Wearable Electronic Devices, Internet of Things, Artificial Intelligence, Delivery of Health Care, Humans
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
