Login Register Cart 0
Generic placeholder image

Recent Advances in Computer Science and Communications

Editor-in-Chief

ISSN (Print): 2666-2558
ISSN (Online): 2666-2566

Back
Journal Home About Journal Editorial Board Current Issue Volumes/Issues
Subscribe
Review Article

The Amalgamation of Federated Learning and Explainable Artificial Intelligence for the Internet of Medical Things: A Review

Author(s): Chemmalar Selvi Govardanan, Ramalingam Murugan, Gokul Yenduri, Deepti Raj Gurrammagari, Dasari Bhulakshmi, Dasaradharami Reddy Kandati, Yarradoddi Supriya, Thippa Reddy Gadekallu, Rajkumar Singh Rathore and Rutvij H Jhaveri*

Volume 17, Issue 4, 2024

Published on: 12 December, 2023

Article ID: e121223224367 Pages: 19

DOI: 10.2174/0126662558266152231128060222

Price: $65

conference banner
Abstract

The Internet of Medical Things (IoMT) has emerged as a paradigm shift in healthcare, integrating the Internet of Things (IoT) with medical devices, sensors, and healthcare systems. From peripheral devices that monitor vital signs to remote patient monitoring systems and smart hospitals, IoMT provides a vast array of applications that empower healthcare professionals. However, the integration of IoMT presents numerous obstacles, such as data security, privacy concerns, interoperability, scalability, and ethical considerations. For the successful integration and deployment of IoMT, addressing these obstacles is essential. Federated Learning (FL) permits collaborative model training while maintaining data privacy in distributed environments like IoMT. By incorporating Explainable Artificial Intelligence (XAI) techniques, the resulting models become more interpretable and transparent, enabling healthcare professionals to comprehend the underlying decision-making processes. This integration not only improves the credibility of Artificial Intelligence models but also facilitates the detection of biases, errors, and peculiar patterns in the data. The combination of FL and XAI contributes to the development of more privacy-preserving, trustworthy, and explainable AI systems, which are essential for the development of dependable and ethically sound IoMT applications. Hence, the aim of this paper is to conduct a literature review on the amalgamation of FL and XAI for IoMT.

Graphical Abstract

[1]
B. Bhushan, A. Kumar, A.K. Agarwal, A. Kumar, P. Bhattacharya, and A. Kumar, "Towards a secure and sustainable internet of medical things (iomt): Requirements, design challenges, security techniques, and future trends", Sustainability, vol. 15, no. 7, p. 6177, 2023.
[http://dx.doi.org/10.3390/su15076177]
[2]
H.Y. Lee, K.H. Lee, K.H. Lee, U. Erdenbayar, S. Hwang, E.Y. Lee, J.H. Lee, H.J. Kim, S.B. Park, J.W. Park, T.Y. Chung, T.H. Kim, and H. Youk, "Internet of medical things-based real-time digital health service for precision medicine: Empirical studies using MEDBIZ platform", Digit. Health, vol. 9, 2023.
[http://dx.doi.org/10.1177/20552076221149659] [PMID: 36644659]
[3]
K.S. Akhras, A.A. Alsheikh-Ali, and S. Kabbani, "Use of real-world evidence for healthcare decision-making in the Middle East: practical considerations and future directions", Expert Rev. Pharmacoecon. Outcomes Res., vol. 19, no. 3, pp. 245-250, 2019.
[http://dx.doi.org/10.1080/14737167.2019.1568243] [PMID: 30626231]
[4]
G. Yenduri, R. Kaluri, D.S. Rajput, K. Lakshmanna, T.R. Gadekallu, M. Mahmud, and D.J. Brown, "From assistive technologies to metaverse: Technologies in inclusive higher education for students with specific learning difficulties", 2305.11057, 2023.
[5]
A.A. Elias, and S. Nanda, "Adoption of internet of medical things: A systems thinking approach", J. Global Inform. Tech. Manag., vol. 26, no. 1, pp. 9-24, 2023.
[http://dx.doi.org/10.1080/1097198X.2023.2166750]
[6]
J.B. Awotunde, C. Chakraborty, M. AbdulRaheem, G. Jimoh, D. Oladipo, and A. Bhoi, "Internet of medical things for enhanced smart healthcare systems", In: Implementation of Smart Healthcare Systems using AI, IoT, and Blockchain., Elsevier, 2023, pp. 1-28.
[http://dx.doi.org/10.1016/B978-0-323-91916-6.00009-6]
[7]
X. Wang, and Y. Song, "Edge-assisted iomt-based smart-home monitoring system for the elderly with chronic diseases", IEEE Sens. Lett., vol. 7, no. 2, pp. 1-4, 2023.
[http://dx.doi.org/10.1109/LSENS.2023.3240670]
[8]
J. Srivastava, and S. Routray, "Ai enabled internet of medical things framework for smart healthcare", In First International Conference, ICIICC 2022 Bhubaneswar, Odisha, India, December 16-17, 2022, pp. 30-46
[http://dx.doi.org/10.1007/978-3-031-23233-6_3]
[9]
F. Kamalov, B. Pourghebleh, M. Gheisari, Y. Liu, and S. Moussa, "Internet of medical things privacy and security: Challenges, solutions, and future trends from a new perspective", Sustainability (Basel), vol. 15, no. 4, p. 3317, 2023.
[http://dx.doi.org/10.3390/su15043317]
[10]
G. Yenduri, R. Kaluri, T.R. Gadekallu, M. Mahmud, and D.J. Brown, "Blockchain for software maintainability in healthcare", In 24th International Conference on Distributed Computing and Networking, 2023, pp. 420-424
[11]
T. Shaik, X. Tao, N. Higgins, L. Li, R. Gururajan, X. Zhou, and U.R. Acharya, "Remote patient monitoring using artificial intelligence: Current state, applications, and challenges", Wiley Interdiscip. Rev. Data Min. Knowl. Discov., vol. 13, no. 2, p. e1485, 2023.
[http://dx.doi.org/10.1002/widm.1485]
[12]
Y. Djenouri, A. Belhadi, G. Srivastava, and J.C.W. Lin, "A secure parallel pattern mining system for medical internet of things", IEEE/ACM Trans. Comput. Biol. Bioinformatics, vol. PP, pp. 1-12, 2023.
[http://dx.doi.org/10.1109/TCBB.2022.3233803] [PMID: 37018269]
[13]
A.K. Nair, J. Sahoo, and E.D. Raj, "Privacy preserving Federated Learning framework for IoMT based big data analysis using edge computing", Comput. Stand. Interfaces, vol. 86, p. 103720, 2023.
[http://dx.doi.org/10.1016/j.csi.2023.103720]
[14]
O.A. Wahab, A. Mourad, H. Otrok, and T. Taleb, "Federated machine learning: Survey, multi-level classification, desirable criteria and future directions in communication and networking systems", IEEE Commun. Surv. Tutor., vol. 23, no. 2, pp. 1342-1397, 2021.
[http://dx.doi.org/10.1109/COMST.2021.3058573]
[15]
R. Saade, K. Salhab, and Z. Nakad, "A voice-controlled mobile iot guider system for visually impaired students", In 2018 IEEE International Multidisciplinary Conference on Engineering Technology (IMCET) IEEE, year.2018, pp. 1-6
[http://dx.doi.org/10.1109/IMCET.2018.8603052]
[16]
G. Yenduri, G. Srivastava, P.K.R. Maddikunta, R.H. Jhaveri, W. Wang, A.V. Vasilakos, and T.R. Gadekallu, "Generative pretrained transformer: A comprehensive review on enabling technologies, potential applications, emerging challenges, and future directions", 2305.10435, 2023.
[17]
H.V. Tran, G. Kaddoum, H. Elgala, C. Abou-Rjeily, and H. Kaushal, "Lightwave power transfer for federated learning-based wireless networks", IEEE Commun. Lett., vol. 24, no. 7, pp. 1472-1476, 2020.
[http://dx.doi.org/10.1109/LCOMM.2020.2985698]
[18]
C. Dhasarathan, M.K. Hasan, S. Islam, S. Abdullah, S. Khapre, D. Singh, A.A. Alsulami, and A. Alqahtani, "User privacy prevention model using supervised federated learning-based block chain approach for internet of Medical Things", CAAI Trans. Intell. Technol., p. cit2.12218, 2023.
[http://dx.doi.org/10.1049/cit2.12218]
[19]
S.A. Rahman, H. Tout, C. Talhi, and A. Mourad, "Internet of things intrusion detection: Centralized, on-device, or federated learning?", IEEE Netw., vol. 34, no. 6, pp. 310-317, 2020.
[http://dx.doi.org/10.1109/MNET.011.2000286]
[20]
N. Sharma, S. Tiwari, M. Ilyas, R. Raghuvanshi, and A. Verma, Iomt implementation: Technological overview for healthcare systems.., Federated Learning for Internet of Medical Things, 2023, pp. 65-83.
[http://dx.doi.org/10.1201/9781003303374-4]
[21]
A. Hammoud, H. Otrok, A. Mourad, and Z. Dziong, "On demand fog federations for horizontal federated learning in iov", IEEE Trans. Netw. Serv. Manag., vol. 19, no. 3, pp. 3062-3075, 2022.
[http://dx.doi.org/10.1109/TNSM.2022.3172370]
[22]
Y. Supriya, and T.R. Gadekallu, Particle swarm-based federated learning approach for early detection of forest fires, 2023.
Available from: https://www.mdpi.com/2071-1050/15/2/964 [http://dx.doi.org/10.3390/su15020964]
[23]
M.M. Yunis, R. El-Khalil, and M. Ghanem, Towards a conceptual framework on the importance of privacy and security concerns in audit data analytics., Adnan Kassar School of Business Lebanese American University: Beirut, Lebanon, 2021.
[24]
S. AbdulRahman, H. Tout, H. Ould-Slimane, A. Mourad, C. Talhi, and A. Guizani, "A survey on federated learning: The journey from centralized to distributed on-site learning and beyond,”", IEEE Internet Things J., vol. 8, no. 7, pp. 5476-5497, 2020.
[25]
V.K. Prasad, P. Bhattacharya, D. Maru, S. Tanwar, A. Verma, A. Singh, A.K. Tiwari, R. Sharma, A. Alkhayyat, F-E. Țurcanu, and M.S. Raboaca, "Federated learning for the internet-of-medical-things: A survey", Mathematics, vol. 11, no. 1, p. 151, 2022.
[http://dx.doi.org/10.3390/math11010151]
[26]
Y.B. Zikria, M.K. Afzal, and S.W. Kim, "Internet of multimedia things (iomt): Opportunities, challenges and solutions", Sensors (Basel), vol. 20, no. 8, p. 2334, 2020.
[http://dx.doi.org/10.3390/s20082334] [PMID: 32325944]
[27]
S. Razdan, and S. Sharma, "Internet of medical things (iomt): Overview, emerging technologies, and case studies", IETE Tech. Rev., vol. 39, no. 4, pp. 775-788, 2022.
[http://dx.doi.org/10.1080/02564602.2021.1927863]
[28]
A. Rahman, M.S. Hossain, G. Muhammad, D. Kundu, T. Debnath, M. Rahman, M.S.I. Khan, P. Tiwari, and S.S. Band, "Federated learning-based AI approaches in smart healthcare: Concepts, taxonomies, challenges and open issues", Cluster Comput., pp. 1-41, 2022.
[PMID: 35996680]
[29]
N. Li, M. Xu, Q. Li, J. Liu, S. Bao, Y. Li, J. Li, and H. Zheng, "A review of security issues and solutions for precision health in Internet-of-Medical-Things systems", Security and Safety, vol. 2, p. 2022010, 2023.
[http://dx.doi.org/10.1051/sands/2022010]
[30]
"Data types that major ai models feed on to function", Available from: https://www.anolytics.ai/blog/top-8- data-types-that-major-ai-models-feed-on-to-function/ (Accessed on 12.07.2023).
[31]
M. Amjad, M.A. Aslam, and A. Akhtar, "Impact of federated learning on patient healthcare monitoring model approach", International J. Comput. and Inno. Sci., vol. 2, no. 2, pp. 1-6, 2023.
[32]
W. Saeed, and C. Omlin, "Explainable AI (XAI): A systematic meta-survey of current challenges and future opportunities", Knowl. Base. Syst., vol. 263, p. 110273, 2023.
[http://dx.doi.org/10.1016/j.knosys.2023.110273]
[33]
A. Rauniyar, D.H. Hagos, and D. Jha, "Federated learning for medical applications: A taxonomy, current trends, challenges, and future research directions,”", 2208.03392, 2022.
[34]
D.R. Kandati, and T.R. Gadekallu, "Genetic clustered federated learning for covid-19 detection", Electronics, vol. 11, no. 17, p. 2714, 2022.
[http://dx.doi.org/10.3390/electronics11172714]
[35]
H. Ali, T. Alam, M. Househ, and Z. Shah, "Federated learning and internet of medical things—opportunities and challenges", Stud. Health Technol. Inform., vol. 295, pp. 201-204, 2022.
[http://dx.doi.org/10.3233/SHTI220697] [PMID: 35773843]
[36]
A.R. Javed, F. Shahzad, S. Rehman, Y.B. Zikria, I. Razzak, Z. Jalil, and G. Xu, "Future smart cities: Requirements, emerging technologies, applications, challenges, and future aspects", Cities, vol. 129, p. 103794, 2022.
[http://dx.doi.org/10.1016/j.cities.2022.103794]
[37]
S. Rachakonda, S. Moorthy, A. Jain, A. Bukharev, A. Bucur, F. Manni, T.M. Quiterio, L. Joosten, and N.I. Mendez, "Privacy enhancing and scalable federated learning to accelerate ai implementation in crosssilo and iomt environments", IEEE J. Biomed. Health Inform., vol. 27, no. 2, pp. 744-755, 2023.
[http://dx.doi.org/10.1109/JBHI.2022.3185418] [PMID: 35731757]
[38]
D.R. Kandati, and T.R. Gadekallu, "Federated learning approach for early detection of chest lesion caused by COVID-19 infection using particle swarm optimization", Electronics, vol. 12, no. 3, p. 710, 2023.
[http://dx.doi.org/10.3390/electronics12030710]
[39]
K. Dasaradharami Reddy, and T.R. Gadekallu, A comprehensive survey on federated learning techniques for healthcare informatics., vol. Vol. 2023., Computational Intelligence and Neuroscience, 2023.
[40]
S.K. Jagatheesaperumal, Q.V. Pham, R. Ruby, Z. Yang, C. Xu, and Z. Zhang, "Explainable ai over the internet of things (iot): Overview, state-of-the-art and future directions", IEEE Open J. Commun. Soc., vol. 3, pp. 2106-2136, 2022.
[http://dx.doi.org/10.1109/OJCOMS.2022.3215676]
[41]
T. Hulsen, "Explainable artificial intelligence (xai) in healthcare", 202303.0116.v1, 2023.
[http://dx.doi.org/10.20944/preprints202303.0116.v1]
[42]
H. Felzmann, E.F. Villaronga, C. Lutz, and A. Tamò-Larrieux, "Transparency you can trust: Transparency requirements for artificial intelligence between legal norms and contextual concerns", Big Data Soc., vol. 6, no. 1, 2019.
[http://dx.doi.org/10.1177/2053951719860542]
[43]
L. Vigano, and D. Magazzeni, "Explainable security", In 2020 IEEE European Symposium on Security and Privacy Workshops (EuroS&PW) IEEE, 2020, pp. 293-300
[http://dx.doi.org/10.1109/EuroSPW51379.2020.00045]
[44]
J. Druce, M. Harradon, and J. Tittle, "Explainable artificial intelligence (xai) for increasing user trust in deep reinforcement learning driven autonomous systems", 2106.03775, 2021.
[45]
N. Rieke, J. Hancox, W. Li, F. Milletarì, H.R. Roth, S. Albarqouni, S. Bakas, M.N. Galtier, B.A. Landman, K. Maier-Hein, S. Ourselin, M. Sheller, R.M. Summers, A. Trask, D. Xu, M. Baust, and M.J. Cardoso, "The future of digital health with federated learning", NPJ Digit. Med., vol. 3, no. 1, p. 119, 2020.
[http://dx.doi.org/10.1038/s41746-020-00323-1] [PMID: 33015372]
[46]
B. Liu, L. Wang, and M. Liu, "Lifelong federated reinforcement learning: A learning architecture for navigation in cloud robotic systems", IEEE Robot. Autom. Lett., vol. 4, no. 4, pp. 4555-4562, 2019.
[http://dx.doi.org/10.1109/LRA.2019.2931179]
[47]
S. Caldas, S.M.K. Duddu, P. Wu, T. Li, J. Konecnˇ, H. B. V. McMahan, and A. Talwalkar, "Leaf: A benchmark for federated settings,”", 1812.01097, 2018.
[48]
E. Bakopoulou, B. Tillman, and A. Markopoulou, "A federated learning approach for mobile packet classification", 1907.13113.
[49]
Y. Liu, J.J.Q. Yu, J. Kang, D. Niyato, and S. Zhang, "Privacy-preserving traffic flow prediction: A federated learning approach", IEEE Internet Things J., vol. 7, no. 8, pp. 7751-7763, 2020.
[http://dx.doi.org/10.1109/JIOT.2020.2991401]
[50]
M. Chen, Z. Yang, W. Saad, C. Yin, H.V. Poor, and S. Cui, "Performance optimization of federated learning over wireless networks", In 2019 IEEE Global Communications Conference (GLOBECOM) IEEE, year.2019, pp. 1-6
[http://dx.doi.org/10.1109/GLOBECOM38437.2019.9013160]
[51]
Y. Chen, X. Qin, J. Wang, C. Yu, and W. Gao, "Fedhealth: A federated transfer learning framework for wearable healthcare", IEEE Intell. Syst., vol. 35, no. 4, pp. 83-93, 2020.
[http://dx.doi.org/10.1109/MIS.2020.2988604]
[52]
P. Sharma, F.E. Shamout, and D.A. Clifton, "Preserving patient privacy while training a predictive model of in-hospital mortality", 1912.00354, 2019.
[53]
L. Huang, A.L. Shea, H. Qian, A. Masurkar, H. Deng, and D. Liu, "Patient clustering improves efficiency of federated machine learning to predict mortality and hospital stay time using distributed electronic medical records", J. Biomed. Inform., vol. 99, p. 103291, 2019.
[http://dx.doi.org/10.1016/j.jbi.2019.103291] [PMID: 31560949]
[54]
S. Silva, B.A. Gutman, E. Romero, P.M. Thompson, A. Altmann, and M. Lorenzi, "Federated learning in distributed medical databases: Meta-analysis of large-scale subcortical brain data", In: in 2019 IEEE 16th international symposium on biomedical imaging (ISBI 2019). IEEE, 2019, pp. 270-274.
[http://dx.doi.org/10.1109/ISBI.2019.8759317]
[55]
D. Liu, D. Dligach, and T. Miller, "Two-stage federated phenotyping and patient representation learning", In in Proceedings of the conference. Association for Computational Linguistics. Meeting, vol. 2019. NIH Public Access, 2019, p. 283
[http://dx.doi.org/10.18653/v1/W19-5030]
[56]
G. Varoquaux, and V. Cheplygina, "Machine learning for medical imaging: Methodological failures and recommendations for the future", NPJ Digit. Med., vol. 5, no. 1, p. 48, 2022.
[http://dx.doi.org/10.1038/s41746-022-00592-y] [PMID: 35413988]
[57]
G. Yenduri, and T.R. Gadekallu, "A multiple criteria decision analysis based approach to remove uncertainty in SMP models", Sci. Rep., vol. 12, no. 1, p. 22386, 2022.
[http://dx.doi.org/10.1038/s41598-022-27059-0] [PMID: 36572726]
[58]
N. Abi Akl, J. El Khoury, and C. Mansour, "Trip-based prediction of hybrid electric vehicles velocity using artificial neural networks", In in 2021 IEEE 3rd International Multidisciplinary Conference on Engineering Technology (IMCET). IEEE, 2021, pp. 60-65
[59]
M. Gaur, K. Faldu, and A. Sheth, "Semantics of the black-box: Can knowledge graphs help make deep learning systems more interpretable and explainable?", IEEE Internet Comput., vol. 25, no. 1, pp. 51-59, 2021.
[http://dx.doi.org/10.1109/MIC.2020.3031769]
[60]
A. Adadi, and M. Berrada, "Explainable ai for healthcare: from black box to interpretable models", In: Proceedings of ESAI 2019 Fez, Morocco, Springer, 2020, pp. 327-337.
[http://dx.doi.org/10.1007/978-981-15-0947-6_31]
[61]
G. Yang, Q. Ye, and J. Xia, "Unbox the black-box for the medical explainable AI via multi-modal and multi-centre data fusion: A mini-review, two showcases and beyond", Inf. Fusion, vol. 77, pp. 29-52, 2022.
[http://dx.doi.org/10.1016/j.inffus.2021.07.016] [PMID: 34980946]
[62]
A. Holzinger, "The next frontier: Ai we can really trust", In Proceedings, Part I, September 13-17, 2021pp. 427-440
[http://dx.doi.org/10.1007/978-3-030-93736-2_33]
[63]
A. Holzinger, M. Dehmer, F. Emmert-Streib, R. Cucchiara, I. Augenstein, J.D. Ser, W. Samek, I. Jurisica, and N. Díaz-Rodríguez, "Information fusion as an integrative cross-cutting enabler to achieve robust, explainable, and trustworthy medical artificial intelligence", Inf. Fusion, vol. 79, pp. 263-278, 2022.
[http://dx.doi.org/10.1016/j.inffus.2021.10.007]
[64]
Y. Zhao, J. Zhao, L. Jiang, R. Tan, D. Niyato, Z. Li, L. Lyu, and Y. Liu, "Privacy-preserving blockchain-based federated learning for iot devices", IEEE Internet Things J., vol. 8, no. 3, pp. 1817-1829, 2021.
[http://dx.doi.org/10.1109/JIOT.2020.3017377]
[65]
L.U. Khan, S.R. Pandey, N.H. Tran, W. Saad, Z. Han, M.N.H. Nguyen, and C.S. Hong, "Federated learning for edge networks: Resource optimization and incentive mechanism", IEEE Commun. Mag., vol. 58, no. 10, pp. 88-93, 2020.
[http://dx.doi.org/10.1109/MCOM.001.1900649]
[66]
K. Lyko, M. Nitzschke, and A-C. Ngonga Ngomo, "Big data acquisition", In: New Horizons for a Data-Driven Economy: A Roadmap for Usage and Exploitation of Big Data in Europe., 2016, pp. 39-61.
[http://dx.doi.org/10.1007/978-3-319-21569-3_4]
[67]
J. Srivastava, S. Routray, S. Ahmad, and M.M. Waris, Internet of medical things (iomt)-based smart healthcare system: Trends and progress., vol. 2022. Computational Intelligence and Neuroscience, 2022.
[68]
R. Dwivedi, D. Mehrotra, and S. Chandra, "Potential of internet of medical things (IoMT) applications in building a smart healthcare system: A systematic review", J. Oral Biol. Craniofac. Res., vol. 12, no. 2, pp. 302-318, 2022.
[http://dx.doi.org/10.1016/j.jobcr.2021.11.010] [PMID: 34926140]
[69]
C. Thota, R. Sundarasekar, G. Manogaran, R. Varatharajan, and M. Priyan, Centralized fog computing security platform for iot and cloud in healthcare system.Fog computing: Breakthroughs in research and practice., IGI global, 2018, pp. 365-378.
[http://dx.doi.org/10.4018/978-1-5225-5649-7.ch018]
[70]
Y.K. Alotaibi, and F. Federico, "The impact of health information technology on patient safety", Saudi Med. J., vol. 38, no. 12, pp. 1173-1180, 2017.
[http://dx.doi.org/10.15537/smj.2017.12.20631] [PMID: 29209664]
[71]
R. Hireche, H. Mansouri, and A.S.K. Pathan, "Security and privacy management in internet of medical things (iomt): A synthesis", Journal of Cybersecurity and Privacy, vol. 2, no. 3, pp. 640-661, 2022.
[http://dx.doi.org/10.3390/jcp2030033]
[72]
M. Singh, N. Sukhija, A. Sharma, M. Gupta, and P.K. Aggarwal, Security and privacy requirements for iomt-based smart healthcare system: Challenges, solutions, and future scope.Big Data Analysis for Green Computing., CRC Press, 2021, pp. 17-37.
[http://dx.doi.org/10.1201/9781003032328-2]
[73]
J. Ahmed, T.N. Nguyen, B. Ali, M.A. Javed, and J. Mirza, "On the physical layer security of federated learning based iomt networks", IEEE J. Biomed. Health Inform., vol. 27, no. 2, pp. 691-697, 2023.
[http://dx.doi.org/10.1109/JBHI.2022.3173947] [PMID: 35536821]
[74]
L. Xu, X. Zhou, X. Li, R.H. Jhaveri, T.R. Gadekallu, and Y. Ding, "Mobile collaborative secrecy performance prediction for artificial iot networks", IEEE Trans. Industr. Inform., vol. 18, no. 8, pp. 5403-5411, 2022.
[http://dx.doi.org/10.1109/TII.2021.3128506]
[75]
L. Li, Y. Fan, and K-Y. Lin, "A survey on federated learning", In in 2020 IEEE 16th International Conference on Control & Automation (ICCA). IEEE, 2020, pp. 791-796
[http://dx.doi.org/10.1109/ICCA51439.2020.9264412]
[76]
T. Li, A.K. Sahu, A. Talwalkar, and V. Smith, "Federated learning: Challenges, methods, and future directions", IEEE Signal Process. Mag., vol. 37, no. 3, pp. 50-60, 2020.
[http://dx.doi.org/10.1109/MSP.2020.2975749]
[77]
G. Yenduri, and T.R. Gadekallu, "A review on soft computing approaches for predicting maintainability of software: State‐of‐the-art, technical challenges, and future directions", Expert Syst., vol. 40, no. 7, p. e13250, 2023.
[http://dx.doi.org/10.1111/exsy.13250]
[78]
B.B. Gupta, A. Gaurav, E.C. Mar’ın, and W. Alhalabi, "Novel graphbased machine learning technique to secure smart vehicles in intelligent transportation systems", IEEE Trans. Intell. Transp. Syst., 2022.
[79]
W. Samek, and K-R. Muller, "Towards explainable artificial intelligence", In: Explainable AI: interpreting, explaining and visualizing deep learning., 2019, pp. 5-22.
[80]
H. Wang, X. Li, R.H. Jhaveri, T.R. Gadekallu, M. Zhu, T.A. Ahanger, and S.A. Khowaja, "Sparse Bayesian learning based channel estimation in FBMC/OQAM industrial IoT networks", Comput. Commun., vol. 176, pp. 40-45, 2021.
[http://dx.doi.org/10.1016/j.comcom.2021.05.020]
[81]
A. Barredo Arrieta, N. Díaz-Rodríguez, J. Del Ser, A. Bennetot, S. Tabik, A. Barbado, S. Garcia, S. Gil-Lopez, D. Molina, R. Benjamins, R. Chatila, and F. Herrera, "Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI", Inf. Fusion, vol. 58, pp. 82-115, 2020.
[http://dx.doi.org/10.1016/j.inffus.2019.12.012]
[82]
A.E. Khaled, "Internet of medical things (iomt): Overview, taxonomies, and classifications", J. Comp. Communicat., vol. 10, no. 8, pp. 64-89, 2022.
[http://dx.doi.org/10.4236/jcc.2022.108005]
[83]
P. Khatiwada, and B. Yang, "An overview on security and privacy of data in iomt devices: Performance metrics, merits, demerits, and challenges", In: pHealth., 2022, pp. 126-136.
[84]
S.A. Wagan, J. Koo, I.F. Siddiqui, M. Attique, D.R. Shin, and N.M.F. Qureshi, "Internet of medical things and trending converged technologies: A comprehensive review on real-time applications", J. King Saud University-Comp.and Information Sci., 2022.
[http://dx.doi.org/10.1016/j.jksuci.2022.09.005]
[85]
J.N.S. Rubí, and P.R.L. Gondim, "Iomt platform for pervasive healthcare data aggregation, processing, and sharing based on onem2m and openehr", Sensors (Basel), vol. 19, no. 19, p. 4283, 2019.
[http://dx.doi.org/10.3390/s19194283] [PMID: 31623304]
[86]
J. Almalki, W. Al Shehri, R. Mehmood, K. Alsaif, S.M. Alshahrani, N. Jannah, and N.A. Khan, "Enabling blockchain with iomt devices for healthcare", Information, vol. 13, no. 10, p. 448, 2022.
[http://dx.doi.org/10.3390/info13100448]
[87]
P. Pritika, B. Shanmugam, and S. Azam, "Risk assessment of heterogeneous iomt devices: A review", Technologies, vol. 11, no. 1, p. 31, 2023.
[http://dx.doi.org/10.3390/technologies11010031]
[88]
N. Truong, K. Sun, S. Wang, F. Guitton, and Y. Guo, "Privacy preservation in federated learning: An insightful survey from the GDPR perspective", Comput. Secur., vol. 110, p. 102402, 2021.
[http://dx.doi.org/10.1016/j.cose.2021.102402]
[89]
S. Kalra, J. Wen, J.C. Cresswell, M. Volkovs, and H.R. Tizhoosh, "Proxyfl: decentralized federated learning through proxy model sharing", 2111.11343, 2021.
[http://dx.doi.org/10.21203/rs.3.rs-1168002/v1]
[90]
P.N. Srinivasu, N. Sandhya, R.H. Jhaveri, and R. Raut, "From blackbox to explainable ai in healthcare: Existing tools and case studies", Mob. Inf. Syst., vol. 2022, pp. 1-20, 2022.
[http://dx.doi.org/10.1155/2022/8167821]
[91]
E. Onose, Explainability and auditability in ml: Definitions, techniques, and tools, 2021. Available from: https://neptune. ai/blog/explainabilityauditability-ml-definitions-techniques-tools
[92]
A.R. Javed, M.U. Sarwar, M.O. Beg, M. Asim, T. Baker, and H. Tawfik, "A collaborative healthcare framework for shared healthcare plan with ambient intelligence", Hum. Cent. Comput. Informat. Sci., vol. 10, no. 1, p. 40, 2020.
[http://dx.doi.org/10.1186/s13673-020-00245-7]
[93]
K. Wei, L. Zhang, Y. Guo, and X. Jiang, "Health monitoring based on internet of medical things: Architecture, enabling technologies, and applications", IEEE Access, vol. 8, pp. 27468-27478, 2020.
[http://dx.doi.org/10.1109/ACCESS.2020.2971654]
[94]
C. Butpheng, K.H. Yeh, and H. Xiong, "Security and privacy in iotcloud-based e-health systems—a comprehensive review", Symmetry, vol. 12, no. 7, p. 1191, 2020.
[http://dx.doi.org/10.3390/sym12071191]
[95]
B. Tay, and A. Mourad, "Intelligent performance-aware adaptation of control policies for optimizing banking teller process using machine learning", IEEE Access, vol. 8, pp. 153403-153412, 2020.
[http://dx.doi.org/10.1109/ACCESS.2020.3015616]
[96]
N. Kasoju, N. Remya, R. Sasi, S. Sujesh, B. Soman, C. Kesavadas, C. Muraleedharan, P.H. Varma, and S. Behari, Digital health: trends, opportunities and challenges in medical devices, pharma and biotechnology., CSI Transactions on ICT, 2023, pp. 1-20.
[97]
M. Majid, S. Habib, A.R. Javed, M. Rizwan, G. Srivastava, T.R. Gadekallu, and J.C.W. Lin, "Applications of wireless sensor networks and internet of things frameworks in the industry revolution 4.0: A systematic literature review", Sensors, vol. 22, no. 6, p. 2087, 2022.
[http://dx.doi.org/10.3390/s22062087] [PMID: 35336261]
[98]
P.Y. Cheah, N. Jatupornpimol, B. Hanboonkunupakarn, N. Khirikoekkong, P. Jittamala, S. Pukrittayakamee, N.P.J. Day, M. Parker, and S. Bull, "Challenges arising when seeking broad consent for health research data sharing: A qualitative study of perspectives in Thailand", BMC Med. Ethics, vol. 19, no. 1, p. 86, 2018.
[http://dx.doi.org/10.1186/s12910-018-0326-x] [PMID: 30404642]
[99]
K. Spencer, C. Sanders, E.A. Whitley, D. Lund, J. Kaye, and W.G. Dixon, "Patient perspectives on sharing anonymized personal health data using a digital system for dynamic consent and research feedback: A qualitative study", J. Med. Internet Res., vol. 18, no. 4, p. e66, 2016.
[http://dx.doi.org/10.2196/jmir.5011] [PMID: 27083521]
[100]
A. Tase, P. Buckle, M.Z. Ni, and G.B. Hanna, "Medical device error and failure reporting: Learning from the car industry", J. Patient Saf. Risk Manag., vol. 26, no. 3, pp. 135-141, 2021.
[http://dx.doi.org/10.1177/25160435211008273]
[101]
J. Haughey, K. Taylor, M. Dohrmann, and G. Snyder, Medtech and the internet of medical things: How connected medical devices are transforming health care., Deloitte, 2018.
[102]
A. Blanco-Justicia, J. Domingo-Ferrer, S. Martínez, D. Sánchez, A. Flanagan, and K.E. Tan, "Achieving security and privacy in federated learning systems: Survey, research challenges and future directions", Eng. Appl. Artif. Intell., vol. 106, p. 104468, 2021.
[http://dx.doi.org/10.1016/j.engappai.2021.104468]
[103]
J. Zhang, H. Zhu, F. Wang, J. Zhao, Q. Xu, and H. Li, Security and privacy threats to federated learning: Issues, methods, and challenges., vol. 2022. Security and Communication Networks, 2022.
[104]
A.M. Antoniadi, Y. Du, Y. Guendouz, L. Wei, C. Mazo, B.A. Becker, and C. Mooney, "Current challenges and future opportunities for xai in machine learning-based clinical decision support systems: A systematic review", Appl. Sci., vol. 11, no. 11, p. 5088, 2021.
[http://dx.doi.org/10.3390/app11115088]
[105]
A. Costin, and C. Eastman, "Need for interoperability to enable seamless information exchanges in smart and sustainable urban systems", J. Comput. Civ. Eng., vol. 33, no. 3, p. 04019008, 2019.
[http://dx.doi.org/10.1061/(ASCE)CP.1943-5487.0000824]
[106]
J.N.S. Rub’ı, and P.R.L. Gondim, "Interoperable internet of medical things platform for e-health applications", Int. J. Distrib. Sens. Netw., vol. 16, no. 1, p. 1550147719889591, 2020.
[107]
C. Dinh-Le, R. Chuang, S. Chokshi, and D. Mann, "Wearable health technology and electronic health record integration: Scoping review and future directions", JMIR Mhealth Uhealth, vol. 7, no. 9, p. e12861, 2019.
[http://dx.doi.org/10.2196/12861] [PMID: 31512582]
[108]
A. Velinov, A. Mileva, S. Wendzel, and W. Mazurczyk, "Covert channels in the mqtt-based internet of things", IEEE Access, vol. 7, pp. 161899-161915, 2019.
[http://dx.doi.org/10.1109/ACCESS.2019.2951425]
[109]
X. Gansel, M. Mary, and A. van Belkum, "Semantic data interoperability, digital medicine, and e-health in infectious disease management: a review", Eur. J. Clin. Microbiol. Infect. Dis., vol. 38, no. 6, pp. 1023-1034, 2019.
[http://dx.doi.org/10.1007/s10096-019-03501-6] [PMID: 30771124]
[110]
T. Górski, "´ “Uml profile for messaging patterns in service-oriented architecture, microservices, and internet of things,”", Appl. Sci., vol. 12, no. 24, p. 12790, 2022.
[http://dx.doi.org/10.3390/app122412790]
[111]
E. Mbunge, B. Muchemwa, S. Jiyane, and J. Batani, "Sensors and healthcare 5.0: Transformative shift in virtual care through emerging digital health technologies", Global Health Journal, vol. 5, no. 4, pp. 169-177, 2021.
[http://dx.doi.org/10.1016/j.glohj.2021.11.008]
[112]
A. Torab-Miandoab, T. Samad-Soltani, A. Jodati, and P. Rezaei-Hachesu, "Interoperability of heterogeneous health information systems: A systematic literature review", BMC Med. Inform. Decis. Mak., vol. 23, no. 1, p. 18, 2023.
[http://dx.doi.org/10.1186/s12911-023-02115-5] [PMID: 36694161]
[113]
O. Kumar, G. Sudhakaran, V. Balaji, and A. Nhaveen, Securing health care data through blockchain enabled collaborative machine learning, 2022.
https://assets.researchsquare.com/files/rs-2205379/v1/b2b374f8-3fe0-4441-acb7-ea93c0d6f338.pdf?c=1697491769 [http://dx.doi.org/10.21203/rs.3.rs-2205379/v1]
[114]
M. Orabi, J. Khalife, A.A. Abdallah, Z.M. Kassas, and S.S. Saab, "A machine learning approach for gps code phase estimation in multipath environments", In Location and Navigation Symposium (PLANS) IEEE, 2020, pp. 1224-1229
[http://dx.doi.org/10.1109/PLANS46316.2020.9110155]
[115]
E. Khodabandehloo, D. Riboni, and A. Alimohammadi, "HealthXAI: Collaborative and explainable AI for supporting early diagnosis of cognitive decline", Future Gener. Comput. Syst., vol. 116, pp. 168-189, 2021.
[http://dx.doi.org/10.1016/j.future.2020.10.030]
[116]
A.S. Albahri, A.M. Duhaim, M.A. Fadhel, A. Alnoor, N.S. Baqer, L. Alzubaidi, O.S. Albahri, A.H. Alamoodi, J. Bai, A. Salhi, J. Santamaría, C. Ouyang, A. Gupta, Y. Gu, and M. Deveci, "A systematic review of trustworthy and explainable artificial intelligence in healthcare: Assessment of quality, bias risk, and data fusion", Inf. Fusion, vol. 96, pp. 156-191, 2023.
[http://dx.doi.org/10.1016/j.inffus.2023.03.008]
[117]
R.T. Sutton, D. Pincock, D.C. Baumgart, D.C. Sadowski, R.N. Fedorak, and K.I. Kroeker, "An overview of clinical decision support systems: Benefits, risks, and strategies for success", NPJ Digit. Med., vol. 3, no. 1, p. 17, 2020.
[http://dx.doi.org/10.1038/s41746-020-0221-y] [PMID: 32047862]
[118]
A. Wasylewicz, and A. Scheepers-Hoeks, "Clinical decision support systems", In: Fundamentals of clinical data science., 2019, pp. 153-169.
[119]
A. Chaddad, Explainable, domain-adaptive, and federated artificial intelligence in medicine, 2022.
[120]
J.H. Yoo, H. Jeong, J. Lee, and T-M. Chung, "Federated learning: Issues in medical application", In: T.K. Dang, J. Kung, T.M. Chung, M. Takizawa, Eds., Future Data and Security Engineering., Springer International Publishing: Cham, 2021, pp. 3-22.
[http://dx.doi.org/10.1007/978-3-030-91387-8_1]
[121]
E. Baumfeld Andre, N. Carrington, F.S. Siami, J.C. Hiatt, C. McWilliams, C. Hiller, A. Surinach, A. Zamorano, C.L. Pashos, and W.L. Schulz, "The current landscape and emerging applications for real-world data in diagnostics and clinical decision support and its impact on regulatory decision making", Clin. Pharmacol. Ther., vol. 112, no. 6, pp. 1172-1182, 2022.
[http://dx.doi.org/10.1002/cpt.2565] [PMID: 35213741]
[122]
S.J. Kim, S.J. Choi, J.S. Jang, H.J. Cho, and I.D. Kim, "Innovative nanosensor for disease diagnosis", Acc. Chem. Res., vol. 50, no. 7, pp. 1587-1596, 2017.
[http://dx.doi.org/10.1021/acs.accounts.7b00047] [PMID: 28481075]
[123]
G. Srivastava, R.H. Jhaveri, S. Bhattacharya, S. Pandya, P.K.R. Maddikunta, G. Yenduri, J.G. Hall, M. Alazab, and T.R. Gadekallu, "Xai for cybersecurity: state of the art, challenges, open issues and future directions", 2206.03585, 2022.
[124]
A. Raza, K.P. Tran, L. Koehl, and S. Li, "AnoFed: Adaptive anomaly detection for digital health using transformer-based federated learning and support vector data description", Eng. Appl. Artif. Intell., vol. 121, p. 106051, 2023.
[http://dx.doi.org/10.1016/j.engappai.2023.106051]
[125]
D. Saraswat, P. Bhattacharya, A. Verma, V.K. Prasad, S. Tanwar, G. Sharma, P.N. Bokoro, and R. Sharma, "Explainable ai for healthcare 5.0: Opportunities and challenges", IEEE Access, vol. 10, pp. 84486-84517, 2022.
[http://dx.doi.org/10.1109/ACCESS.2022.3197671]
[126]
A. Raza, K.P. Tran, L. Koehl, and S. Li, "Designing ECG monitoring healthcare system with federated transfer learning and explainable AI", Knowl. Base. Syst., vol. 236, p. 107763, 2022.
[http://dx.doi.org/10.1016/j.knosys.2021.107763]
[127]
R. Chengoden, N. Victor, T. Huynh-The, G. Yenduri, R.H. Jhaveri, M. Alazab, S. Bhattacharya, P. Hegde, P.K.R. Maddikunta, and T.R. Gadekallu, "Metaverse for healthcare: A survey on potential applications, challenges and future directions", IEEE Access, vol. 11, pp. 12765-12795, 2023.
[http://dx.doi.org/10.1109/ACCESS.2023.3241628]
[128]
D. Rotstein, and X. Montalban, "Reaching an evidence-based prognosis for personalized treatment of multiple sclerosis", Nat. Rev. Neurol., vol. 15, no. 5, pp. 287-300, 2019.
[http://dx.doi.org/10.1038/s41582-019-0170-8] [PMID: 30940920]
[129]
Y. Li, M.Y.T. Yip, D.S.W. Ting, and M. Ang, "Artificial intelligence and digital solutions for myopia", Taiwan J. Ophthalmol., vol. 13, no. 2, pp. 142-150, 2023.
[http://dx.doi.org/10.4103/tjo.TJO-D-23-00032] [PMID: 37484621]
[130]
D. Azar, R. Moussa, and G. Jreij, "A comparative study of nine machine learning techniques used for the prediction of diseases", Int. J. Artif. Intell., vol. 16, no. 2, pp. 25-40, 2018.
[131]
W.C. Chung, Y.H. Lin, and S.H. Fang, "Fedism: Enhancing data imbalance via shared model in federated learning", Mathematics, vol. 11, no. 10, p. 2385, 2023.
[http://dx.doi.org/10.3390/math11102385]
[132]
T. Adhikari, "Towards explainable ai: Interpretable models and feature attribution", SSRN, p. 4376176, 2023.
[http://dx.doi.org/10.2139/ssrn.4376176]
[133]
A. Shahzad, Y.S. Lee, M. Lee, Y-G. Kim, and N. Xiong, "Realtime cloud-based health tracking and monitoring system in designed boundary for cardiology patients", J. Sens., vol. 2018, 2018.
[134]
O.C. Madubuike, and C.J. Anumba, "Digital twin–based health care facilities management", J. Comput. Civ. Eng., vol. 37, no. 2, p. 04022057, 2023.
[http://dx.doi.org/10.1061/JCCEE5.CPENG-4842]
[135]
M. W. Bhatt, and S. Sharma, "An iomt-based approach for realtime monitoring using wearable neuro-sensors", J. Healthcare Eng., vol. 2023, 2023.
[136]
V.K. Prasad, J. Solanki, P. Bhattacharya, A. Verma, and M. Bhavsar, “2 artificial intelligence applications for,” Federated Learning for Internet of Medical Things: Concepts., Paradigms, and Solutions, 2023.
[137]
P. Dinh, M.A. Arfaoui, S. Sharafeddine, C.M. Assi, and A. Ghrayeb, "A low-complexity framework for joint user pairing and power control for cooperative noma in 5g and beyond cellular networks", IEEE Trans. Commun., vol. 68, no. 11, pp. 6737-6749, 2020.
[http://dx.doi.org/10.1109/TCOMM.2020.3009262]
[138]
S. Dara, S. Dhamercherla, S.S. Jadav, C.H.M. Babu, and M.J. Ahsan, "Machine learning in drug discovery: A review", Artif. Intell. Rev., vol. 55, no. 3, pp. 1947-1999, 2022.
[http://dx.doi.org/10.1007/s10462-021-10058-4] [PMID: 34393317]
[139]
V. Patel, and M. Shah, "Artificial intelligence and machine learning in drug discovery and development", Intelligent Medicine, vol. 2, no. 3, pp. 134-140, 2022.
[http://dx.doi.org/10.1016/j.imed.2021.10.001]
[140]
M.B. Jamshidi, O. Moztarzadeh, A. Jamshidi, A. Abdelgawad, A.S. El-Baz, and L. Hauer, "Future of drug discovery: The synergy of edge computing, internet of medical things, and deep learning", Future Internet, vol. 15, no. 4, p. 142, 2023.
[http://dx.doi.org/10.3390/fi15040142]
[141]
A. Lakhan, M.A. Mohammed, J. Nedoma, R. Martinek, P. Tiwari, A. Vidyarthi, A. Alkhayyat, and W. Wang, "Federated-learning based privacy preservation and fraud-enabled blockchain iomt system for healthcare", IEEE J. Biomed. Health Inform., 2022.
[PMID: 35394919]
[142]
S. Rani, A. Kataria, S. Kumar, and P. Tiwari, "Federated learning for secure IoMT-applications in smart healthcare systems: A comprehensive review", Knowl. Base. Syst., vol. 274, p. 110658, 2023.
[http://dx.doi.org/10.1016/j.knosys.2023.110658]
[143]
J. L. C. Barcena, Fed-xai: Federated learning of explainable artificial intelligence models, 2022.
[144]
K. Huang, Z. Xiang, W. Deng, C. Yang, and Z. Wang, "False data injection attacks detection in smart grid: A structural sparse matrix separation method", IEEE Trans. Netw. Sci. Eng., vol. 8, no. 3, pp. 2545-2558, 2021.
[http://dx.doi.org/10.1109/TNSE.2021.3098738]
[145]
K. Pan, A. Teixeira, M. Cvetkovic, and P. Palensky, "Cyber risk analysis of combined data attacks against power system state estimation", IEEE Trans. Smart Grid, vol. 10, no. 3, pp. 3044-3056, 2019.
[http://dx.doi.org/10.1109/TSG.2018.2817387]
[146]
J. Nader, M. A. Mezher, and R. El-Khalil, "Towards understanding the impact of industry 4.0 technologies on operational performance: An empirical investigation in the us and eu automotive industry", http://www.ieomsociety.org/brazil2020/papers/803.pdf
[147]
W. Deng, Z. Xiang, K. Huang, J. Liu, C. Yang, and W. Gui, "Detecting intelligent load redistribution attack based on power load pattern learning in cyber-physical power systems", IEEE Trans. Ind. Electron., pp. 1-9, 2023.
[http://dx.doi.org/10.1109/TIE.2023.3294646]

Rights & Permissions Print Cite
© 2024 Bentham Science Publishers | Privacy Policy