Federated learning-based AI approaches in smart healthcare: Concepts, taxonomies, challenges and open issues

May combine two claims.

Due to raw data transfer, the centralized AI approach is inefficient in terms of communication latency and cannot achieve high network scalability.

“Due to raw data transfer, this centralized approach is inefficient in terms of communication latency and cannot achieve high network scalability.”

• cites [5] Rahman, A., Islam, M.J., Saikat Islam Khan, M., Kabir, S., Pritom, A.I., Razaul Karim, M.: Block-sdotcloud: Enhancing security of cloud storage through blockchain-based sdn in iot network. In: 2020 2nd International Conference on Sustainable Technologies for Industry 4.0 (STI), pp. 1–6 (2020). https://doi.org/10.1109/STI50764.2020.9350419 sourceNot checked yet

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• cites [6] Islam, M.J., Rahman, A., Kabir, S., Khatun, A., Pritom, A., Chowdhury, M.: Sdot-nfv: A distributed sdn based security system with iot for smart city environments. GUB Journal of Science and Engineering 7, 27–35 (2021) https://doi.org/10.3329/gubjse.v7i0.54015. https://www.banglajol.info/index.php/GUBJSE/article/view/54015 sourceNot checked yet

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• cites [7] Hossen, R., Whaiduzzaman, M., Uddin, M.N., Islam, M., Faruqui, N., Barros, A., Sookhak, M., Mahi, M., Nayeen, J., et al.: Bdps: An efficient spark-based big data processing scheme for cloud fog-iot orchestration. Information 12(12), 517 (2021) sourceNot checked yet

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Probably not a substantial claim: may not pass S3 (load-bearing).

Researchers have attempted to use AI to boost scientific analysis and analyse potential remedies, thereby improving the overall efficacy of the healthcare industry.

“More specifically, researchers have attempted to use AI to boost scientific analysis and analyse potential remedies, hence improving the overall efficacy of the healthcare industry [16, 17].”

• cites [16] Yu, K.H., Beam, A.L., Kohane, I.S.: Artificial intelligence in healthcare. Nat. Biomed. Eng. 2(10), 719–731 (2018) sourceNot checked yet

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• cites [17] Peng, Y., Zhang, Y., Wang, L.: Guest editorial: Artificial intelligence in biomedical engineering and informatics: An introduction and review. Artificial Intell. Med. 48(2–3), 71–73 (2010) sourceNot checked yet

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Accumulation of system or model parameters creates a global model using a server.

“Accumulation of system or model parameters creates a global model using a server [72].”

• cites [72] Yang, Q., Liu, Y., Chen, T., Tong, Y.: Federated machine learning: Concept and applications. ACM Transactions on Intelligent Systems and Technology (TIST) 10(2), 1–19 (2019) sourceNot checked yet

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Probably not a substantial claim: may not pass S2 (checkable), S4 (based).

This study is the only potential and informative survey that integrates all the considered technologies to the best of the authors' knowledge.

“This study is the only potential and informative survey that integrates all the considered technologies to the best of our knowledge.”

Classified as probably not a substantial claim. May combine two claims.

FL allows for equipping ML models with decentralized data and at the same time protects user confidential data by design.

“FL that has been shown in Fig. 1, it allows for equipping ML models with decentralized data and at the same time protects user confidential data by design.”

Probably not a substantial claim: may not pass S4 (based).

As far as the authors know, no established research provides a complete overview of FL-based AI uses in the sphere of smart healthcare.

“Despite these attempts, as far as we know, no established research provides complete overview of FL-based AI uses in the sphere of smart healthcare.”

Probably not a substantial claim: may not pass S4 (based).

The most recent advancements in FL, such as resource-aware FL, safe and reliable FL, privacy-boosted FL, stimulus-aware FL, and tailored FL, have yet to be extensively investigated.

“The most recent advancements in FL, for instance FL aware of resources, safe and reliable FL, the potential of privacy-boosted FL, stimulus-aware FL, and tailored FL has yet to be extensively investigated.”

Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).

AI and its applications in healthcare have recently made considerable strides.

“AI and its applications in healthcare have recently made considerable strides [19].”

• cites [19] Arikumar, K., Prathiba, S.B., Alazab, M., Gadekallu, T.R., Pandya, S., Khan, J.M., Moorthy, R.S.: Fl-pmi: Federated learning-based person movement identification through wearable devices in smart healthcare systems. Sensors 22(4), 1377 (2022) sourceNot checked yet

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Probably not a substantial claim: may not pass S3 (load-bearing).

The server in FL aggregates the local data into a global network through an iterative process.

“The server in FL aggregates the local data into a global network through an iterative process [91].”

• cites [91] Bonawitz, K., Eichner, H., Grieskamp, W., Huba, D., Ingerman, A., Ivanov, V., Kiddon, C., Konečnỳ, J., Mazzocchi, S., McMahan, H.B., et al.: Towards federated learning at scale: System design. arXiv preprint arXiv:1902.01046 (2019)Not checked yet

  Not checked in the free run.

Classified as probably not a substantial claim.

In practice, the user does not need to share their private data using FL.

“In practice, the user does not need to share their private data using FL.”

Probably not a substantial claim: may not pass S4 (based).

The available literature lacks a comprehensive taxonomy of FL's use in new healthcare applications.

“Furthermore, the available literature lacks a comprehensive taxonomy of FL’s use in new healthcare applications.”

May combine two claims.

FL is capable of providing a suitable balance between precision and usefulness, as well as privacy enhancement, as compared to traditional centralized learning.

“A Reasonable Balance of Accuracy and Utility: FL is capable of providing a suitable balance between precision and usefulness, as well as privacy enhancement, as compared to traditional centralized learning.”

Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.

FL training keeps the model generalizability while sacrificing nominal accuracy.

“Furthermore, FL training keeps the model generalizability while sacrificing nominal accuracy.”

Probably not a substantial claim: may not pass S3 (load-bearing).

In Federated Learning, the information supplied to the server by each participant resembles no original data, rather a trained sub-model.

“In Federated Learning, the information supplied to the server by each participant resembles no original data, rather a trained sub-model.”

Probably not a substantial claim: may not pass S3 (load-bearing).

The ever growing number of IoT resources together with relevant applications means the requirement of processing large amount of data.

“The ever growing number of IoT resources together with relevant applications means the requirement of processing large amount of data [98, 99].”

• cites [98] Rahman, A., Nasir, M.K., Rahman, Z., Mosavi, A., Shahab, S., Minaei-Bidgoli, B.: Distblockbuilding: A distributed blockchain-based sdn-iot network for smart building management. IEEE Access 8, 140008–140018 (2020) sourceNot checked yet

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• cites [99] Rahman, A., Islam, M.J., Rahman, Z., Reza, M.M., Anwar, A., Mahmud, M.P., Nasir, M.K., Noor, R.M.: Distb-condo: Distributed blockchain-based iot-sdn model for smart condominium. IEEE Access 8, 209594–209609 (2020) sourceNot checked yet

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May combine two claims.

FL saves a significant amount of network bandwidth and reduces the risk of network congestion in large healthcare networks.

“As a result, FL saves a significant amount of network bandwidth and reduces the risk of network congestion in large healthcare networks.”

• Also stated in: § e-Healthcare system · plain

  “As a result, FL saves a significant amount of network bandwidth and reduces the risk of network congestion in large healthcare networks.”

May combine two claims.

Federated Learning permits asynchronous transmission, which reduces requirements for communication purposes.

“Simultaneously, Federated Learning permits asynchronous transmission, which reduces requirements for communication purposes [92].”

• cites [92] Shokri, R., Shmatikov, V.: Privacy-preserving deep learning. In: Proceedings of the 22nd ACM SIGSAC conference on computer and communications security, pp. 1310–1321 (2015) sourceNot checked yet

  Not checked in the free run.

May combine two claims.

As a result of FL, the risk of personal data being leaked to an external source is reduced, and privacy is ensured.

“As a result, risk of personal data being leaked to an external source is reduced, and privacy is ensured.”

Probably not a substantial claim: may not pass S4 (based).

The framework in FL allows the users to access data without gaining personal information regarding other clients.

“The framework in FL allows the users to access data without gaining personal information regarding other clients.”

May combine two claims.

FL is highly effective in areas where decision making is based on substantial data scattered over a wide range of training nodes while addressing privacy and security concerns.

“FL is highly effective in areas where decision making is based on substantial data scattered over a wide range of training nodes at the same time addressing privacy and security concern [100].”

• cites [100] Shaheen, M., Farooq, M.S., Umer, T., Kim, B.S.: Applications of federated learning; taxonomy, challenges, and research trends. Electronics 11(4), 670 (2022) sourceNot checked yet

  Not checked in the free run.

May combine two claims.

FL is a highly effective method to gather EHR data from hospitals to provide a knowledge-sharing platform without the need of sharing personal data, all while preserving privacy.

“FL is a highly effective method to gather EHR data from hospitals to provide a knowledge-sharing platform without the need of sharing personal data, all while preserving the privacy [104, 105].”

• cites [104] Gruendner, J., Schwachhofer, T., Sippl, P., Wolf, N., Erpenbeck, M., Gulden, C., Kapsner, L.A., Zierk, J., Mate, S., Stürzl, M., et al.: Ketos: Clinical decision support and machine learning as a service-a training and deployment platform based on docker, omop-cdm, and fhir web services. PloS One 14(10), e0223010 (2019) sourceNot checked yet

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• cites [105] Huang, L., Shea, A.L., Qian, H., Masurkar, A., Deng, H., Liu, D.: Patient clustering improves efficiency of federated machine learning to predict mortality and hospital stay time using distributed electronic medical records. J. Biomed Informat. 99, 103291 (2019) sourceNot checked yet

  Not checked in the free run.

Probably not a substantial claim: may not pass S4 (based).

As a result of bandwidth issues, security and storage facility, raw data transmission to a centralized location becomes unrealistic.

“Regardless, as a result of bandwidth issues, security and storage facility, raw data transmission to a centralized location becomes unrealistic.”

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

Federated Learning approach is a concept introduced by Google to allow on-device learning and security of sensitive data.

“Federated Learning approach is a concept introduced by google to allow on-device learning and security of sensitive data [93].”

• cites [93] Konečnỳ, J., McMahan, H.B., Yu, F.X., Richtárik, P., Suresh, A.T., Bacon, D.: Federated learning: Strategies for improving communication efficiency. arXiv preprint arXiv:1610.05492 (2016)Not checked yet

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Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based). May combine two claims.

All these devices mean an exponential amount of generated datasets with an intense opportunity to learn from intelligent systems.

“All these devices mean an exponential amount of generated datasets with an intense opportunity to learn from intelligent systems.”

Probably not a substantial claim: may not pass S3 (load-bearing).

Smart devices with measuring sensors are on the rise.

“Smart devices with measuring sensors are on the rise [117].”

• cites [117] Helbing, D., Frey, B.S., Gigerenzer, G., Hafen, E., Hagner, M., Hofstetter, Y., Van Den Hoven, J., Zicari, R.V., Zwitter, A.: Will democracy survive big data and artificial intelligence? In: Towards digital enlightenment, pp. 73–98. Springer (2019) sourceNot checked yet

  Not checked in the free run.

Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

Because the primary criterion of FL is privacy protection, the aggregation model cannot be realized in practice.

“However, because the primary criterion of FL is privacy protection, the aggregation model cannot be realized in practice.”

• AssumptionRests on an assumption: The primary criterion of FL is privacy protection.

Classified as probably not a substantial claim.

A key concern with traditional AI models that require users to share individual information to a master network is the privacy of users' sensitive information.

“The traditional AI models usually necessitates the users to share individual information to a master network for learning purposes. A key concern with such techniques is the privacy of users sensitive information.”

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

FL has the benefit of distributed data processing and improved privacy factors.

“FL has the benefit of distributed data processing and improved privacy factors.”

Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

Electronic healthcare records from multiple data sources require joint data access and uploading of data to a single database.

“Electronic healthcare records from multiple data sources (several hospitals) require joint data access and uploading of data to a single database.”

Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.

Model-centric, cross-device, horizontal, cross-silo, vertical, data-centric, reinforcement, and other types of Federated Learning exist.

“Model-centric, cross-device, horizontal, cross-silo, vertical, data-centric, reinforcement, and other types of Federated Learning exist.”

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

Sensors capture information about the surroundings and individual activity, which is subsequently evaluated via cloud computing or edge intelligence.

“Sensors capture information about the surroundings and individual activity in this scenario, which is subsequently evaluated via cloud computing or edge intelligence [124].”

• cites [124] Hasan, M., Rahman, A., Islam, M.J.: Distb-cvs: A distributed secure blockchain based online certificate verification system from bangladesh perspective. In: 2020 2nd International Conference on Advanced Information and Communication Technology (ICAICT), pp. 460–465 (2020). https://doi.org/10.1109/ICAICT51780.2020.9333523 sourceNot checked yet

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Through a semantic graph-based AI technique, researchers may effectively accomplish the complex work of outlining the literature on a specific topic.

“Moreover, through a semantic graph-based AI technique, researchers may effectively accomplish the complex work of outlining the literature on a specific topic [128].”

• cites [128] Plaza, L., Díaz, A., Gervás, P.: A semantic graph-based approach to biomedical summarisation. Artificial intell. Med. 53(1), 1–14 (2011) sourceNot checked yet

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They must be flexible enough to operate purely on the basis of matching a model's input to its outputs rather than relying on the model's intrinsic architecture.

“As a result, they must be flexible enough to operate purely on the basis of matching a model’s input to its outputs rather than relying on the model’s intrinsic architecture. [132]”

• cites [132] Dieber, J., Kirrane, S.: Why model why? assessing the strengths and limitations of lime. arXiv preprint arXiv:2012.00093 (2020)Not checked yet

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Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

Although transparency as a feature does not guarantee that a model will be easily explainable, the decisions made by these models are frequently transparent.

“Although transparency as a feature does not guarantee thet a model will be easily explainable, the decisions made by these models are frequently transparent. [130]”

• cites [130] Adadi, A., Berrada, M.: Peeking inside the black-box: a survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) sourceNot checked yet

  Not checked in the free run.

Probably not a substantial claim: may not pass S3 (load-bearing).

K-nearest neighbors (kNN), decision trees, rule-based learning, Bayesian networks, and are examples of typical transparent models.

“Transparent Model: K-nearest neighbors (kNN), decision trees, rule-based learning, Bayesian networks, and are examples of typical transparent models.”

• cites [130] Adadi, A., Berrada, M.: Peeking inside the black-box: a survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) sourceNot checked yet

  Not checked in the free run.

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

AI can gather information from several sources and perform a task, solve critical problems or make a decision without any human instructions.

“AI can gather information from several sources and perform a task, solve critical problems or make a decision without any human instructions [120].”

• cites [120] Kaplan, A., Haenlein, M.: Siri, siri, in my hand: Who’s the fairest in the land? on the interpretations, illustrations, and implications of artificial intelligence. Business Horizons 62(1), 15–25 (2019) sourceNot checked yet

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Probably not a substantial claim: may not pass S3 (load-bearing).

The primary goal of health-related AI applications is to investigate correlations between treatment or preventative strategies and patient outcomes.

“The primary goal of health-related AI applications is to investigate correlations between treatment or preventative strategies and patient outcomes [119].”

• cites [119] Coiera, E.: Guide to medical informatics, the internet and telemedicine. Chapman & Hall, London (1997)Not checked yet

  Not checked in the free run.

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

These models are not transparent, despite the fact that they frequently reach excellent accuracy.

“These models are not transparent, despite the fact that they frequently reach excellent accuracy. [131]”

• cites [131] Angelov, P.P., Soares, E.A., Jiang, R., Arnold, N.I., Atkinson, P.M.: Explainable artificial intelligence: an analytical review. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 11(5), e1424 (2021)Not checked yet

  Not checked in the free run.

Probably not a substantial claim: may not pass S3 (load-bearing).

NIST published Four Rules of XAI in August 2020.

“Because of the growing relevance of this topic, NIST published Four Rules of XAI in August 2020, which describe the following key principles [133] that an AI must follow in order to be designated a XAI:”

• cites [133] Phillips, P.J., Hahn, C.A., Fontana, P.C., Broniatowski, D.A., Przybocki, M.A.: Four principles of explainable artificial intelligence. Gaithersburg, Maryland (2020)Not checked yet

  Not checked in the free run.

Probably not a substantial claim: may not pass S3 (load-bearing).

Random forest, neural networks, Support Vector Machine (SVMs), and other opaque models are common.

“Opaque Model: Random forest, neural networks, Support Vector Machine (SVMs), and other opaque models are common.”

• cites [131] Angelov, P.P., Soares, E.A., Jiang, R., Arnold, N.I., Atkinson, P.M.: Explainable artificial intelligence: an analytical review. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 11(5), e1424 (2021)Not checked yet

  Not checked in the free run.

Probably not a substantial claim: may not pass S3 (load-bearing).

AI can potentially replace humans by working in wide-scale activities in the realm of industry, retail, finance, and healthcare with considerable impacts on system performance and efficiency.

“AI can potentially replace humans by working in wide-scale activities in the realm of industry, retail, finance, and healthcare with considerable impacts on system performance and efficiency [121] that depicted in Fig. 5.”

• cites [121] López-Robles, J.R., Otegi-Olaso, J.R., Gómez, I.P., Cobo, M.J.: 30 years of intelligence models in management and business: A bibliometric review. Int. J. Informat. Manag. 48, 22–38 (2019) sourceNot checked yet

  Not checked in the free run.

Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

With the increased level of data, a system has the potential to increase its accuracy.

“With the increased level of data, a system has the potential to increase its accuracy.”

Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing). May combine two claims.

AI offers untapped potential as a strong tool in biological research, in addition to acting as a eDoctor for illness diagnosis, management, and prognosis.

“AI offers untapped potential as a strong tool in biological research [127], in addition to acting as a eDoctor for illness diagnosis, management, and prognosis.”

• cites [127] Handelman, G., Kok, H., Chandra, R., Razavi, A., Lee, M., Asadi, H.: ed octor: machine learning and the future of medicine. J. Int. Med. 284(6), 603–619 (2018) sourceNot checked yet

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Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

They consist of AI systems that are both accurate and understandable by humans.

“They consist of AI systems that are both accurate and understandable by humans [129].”

• cites [129] Gilpin, L.H., Bau, D., Yuan, B.Z., Bajwa, A., Specter, M., Kagal, L.: Explaining explanations: An overview of interpretability of machine learning. In: 2018 IEEE 5th International Conference on data science and advanced analytics (DSAA), pp. 80–89. IEEE (2018) sourceNot checked yet

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For accuracy over 90%, ML can categorize biological queries into 4 main kinds.

“For accuracy over 90%, ML can be categorize biological queries into 4 main kinds [126].”

• cites [126] Sarrouti, M., El Alaoui, S.O.: A machine learning-based method for question type classification in biomedical question answering. Method. Informat. Med. 56(03), 209–216 (2017) sourceNot checked yet

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Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

The e-healthcare system may give general support on a larger scale, such as management assistance, healthcare service delivery, and so on, as well as specialized help, such as citizen health data.

“The e-healthcare system may give general support on a larger scale, such as management assistance, healthcare service delivery, and so on, as well as specialized help, such as citizen health data [135].”

• cites [135] Muhammad, G., Rahman, S.K.M.M., Alelaiwi, A., Alamri, A.: Smart health solution integrating iot and cloud: a case study of voice pathology monitoring. IEEE Commun. Magazine 55(1), 69–73 (2017) sourceNot checked yet

  Not checked in the free run.

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

The provision of health care via the internet and technology is carried out by combining existing tools and assuring the quality of the services supplied.

“The provision of health care via the internet and technology is carried out by combining existing tools and assuring the quality of the services supplied [136].”

• cites [136] Garrett, L., Chowdhury, A.M.R., Pablos-Méndez, A.: All for universal health coverage. The Lancet 374(9697), 1294–1299 (2009) sourceNot checked yet

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Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).

The implementation of AI in healthcare sectors have created an urge amongst researchers to pursue innovative methods to develop e-healthcare systems.

“The implementation of AI in healthcare sectors [47, 146] have created an urge amongst researchers to pursue innovative methods to develop e-healthcare systems.”

• cites [47] Zhang, K., Liu, X., Shen, J., Li, Z., Sang, Y., Wu, X., Zha, Y., Liang, W., Wang, C., Wang, K., et al.: Clinically applicable ai system for accurate diagnosis, quantitative measurements, and prognosis of covid-19 pneumonia using computed tomography. Cell 181(6), 1423–1433 (2020) sourceNot checked yet

  Not checked in the free run.

• cites [146] Esmaeilzadeh, P.: Use of ai-based tools for healthcare purposes: a survey study from consumers’ perspectives. BMC Med. Informat. Decision Making 20(1), 1–19 (2020) sourceNot checked yet

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May combine two claims.

This approach limits the danger of personal user data being leaked to an external source, ensuring better level of client security.

“This approach limits the danger of personal user data being leaked to an external source, ensuring better level of client security [149].”

• cites [149] Zhao, Y., Zhao, J., Jiang, L., Tan, R., Niyato, D., Li, Z., Lyu, L., Liu, Y.: Privacy-preserving blockchain-based federated learning for iot devices. IEEE Internet of Things J. 8(3), 1817–1829 (2020) sourceNot checked yet

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Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

The IoT and its application for healthcare management services can be divided into three steps: i) gain access to the location of a patient or healthcare staff ii) to identify and authenticate an individual, and iii) to automate data collection using sensory devices.

“The IoT and its application for healthcare management services can be divided into three steps [142]: i) gain access to the location of a patient or healthcare staff ii) to identify and authenticate an individual, and iii) to automate data collection using sensory devices.”

• cites [142] Tsiounia, K., Dimitrioglou, N., Kardaras, D., Barbounaki, S.: A process modelling and analytic hierarchy process approach to investigate the potential of the iot in health services. In: World Congress On Medical Physics and Biomedical Engineering 2018, pp. 381–386. Springer (2019) sourceNot checked yet

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IoT and smart devices are used to transfer the health data of a patient to the cloud server to access health information of an individual.

“IoT and smart devices are used to transfer the health data of a patient to the cloud server to access health information of an individual [141].”

• cites [141] Asghari, P., Rahmani, A.M., Javadi, H.H.S.: Internet of things applications: A systematic review. Comp. Net. 148, 241–261 (2019) sourceNot checked yet

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The prognosis of a patient infected with COVID-19 related illness is limited with high mortality.

“The prognosis of a patient infected with COVID-19 related illness is limited with high mortality [144, 145].”

• cites [144] Muhammad, G., Alqahtani, S., Alelaiwi, A.: Pandemic management for diseases similar to covid-19 using deep learning and 5g communications. IEEE Network 35(3), 21–26 (2021) sourceNot checked yet

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• cites [145] Muhammad, G., Hossain, M.S.: Deep learning-based edge-centric covid-19 like pandemic screening and diagnosis system within b5g framework using blockchain. IEEE Network 35(28), 74–81 (2021) sourceNot checked yet

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The complexities of COVID-19 coupled with scarce resources and rising healthcare cost has encouraged many to access remote health management services using computer or similar devices.

“The complexities of COVID-19 coupled with scarce resources and rising healthcare cost has encouraged many to access remote health management services using computer or similar devices [137–140].”

• cites [137] Rahman, A., Chakraborty, C., Anwar, A., Karim, M., Islam, M., Kundu, D., Rahman, Z., Band, S.S., et al.: Sdn–iot empowered intelligent framework for industry 4.0 applications during covid-19 pandemic. Cluster Computing pp. 1–18 (2021) sourceNot checked yet

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• cites [138] Muhammad, G., Hossain, M.S.: Covid-19 and non-covid-19 classification using multi-layers fusion from lung ultrasound images. Information Fusion 72, 80–88 (2021) sourceNot checked yet

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• cites [139] Hossain, M.J., Wadud, M.A.H., Rahman, A., Ferdous, J., Alam, M.S., Amir Ul Haque Bhuiyan, T.M., Mridha, M.F.: A secured patient’s online data monitoring through blockchain: An intelligent way to store lifetime medical records. In: 2021 International Conference on Science Contemporary Technologies (ICSCT), pp. 1–6 (2021). https://doi.org/10.1109/ICSCT53883.2021.9642537 sourceNot checked yet

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• cites [140] Masud, M., Gaba, G.S., Alqahtani, S., Muhammad, G., Gupta, B., Kumar, P., Ghoneim, A.: A lightweight and robust secure key establishment protocol for internet of medical things in covid-19 patients care. IEEE Internet of Things J. 8(21), 15694–15703 (2021) sourceNot checked yet

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Among the many applications of FL-based healthcare models for prediction, similarity among different patients is one.

“Among the many applications of FL-based healthcare models for prediction, similarity among different patients is one [164].”

• cites [164] Bakopoulou, E., Tillman, B., Markopoulou, A.: A federated learning approach for mobile packet classification. arXiv preprint arXiv:1907.13113 (2019)Not checked yet

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Probably not a substantial claim: may not pass S2 (checkable), S4 (based).

There are various applications in the healthcare sector where FL integration will increase the overall effectiveness of the process analysis.

“There are various applications in the healthcare sector where FL integration will increase the overall effectiveness of the process analysis:”

Classified as probably not a substantial claim. May combine two claims.

In this situation, data is not transported from source to destination in order to merge or generate a dataset for analysis; rather, the local client trains their model with their data and connects with other clients via the server bypassing only the results.

“In this situation, data is not transported from source to destination in order to merge or generate a dataset for analysis; rather, the local client trains their model with their data and connects with other clients via the server bypassing only the results.”

Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based). May combine two claims.

This integration will benefit both suppliers, such as the organization in charge of producing and supplying the necessary equipment to a hospital, and users of the application, such as the general public.

“This integration will benefit both suppliers, such as the organization in charge of producing and supplying the necessary equipment to a hospital, and users of the application, such as the general public.”

Probably not a substantial claim: may not pass S3 (load-bearing).

With sophisticated XAI, the reason for the diagnosis of a certain cancer is apparent and visible to the system's user.

“With sophisticated XAI, the reason for the diagnosis of a certain cancer is apparent and visible to the system’s user [169].”

• cites [169] Muhammad, G., Hossain, M.S., Guizani, N.: Explainable ai and mass surveillance system-based healthcare framework to combat covid-19 like pandemics. IEEE Network 34(4), 126–132 (2020) sourceNot checked yet

  Not checked in the free run.

Probably not a substantial claim: may not pass S2 (checkable), S4 (based). May combine two claims.

As the linking sectors are vast in this era, and data production is also vast, it is difficult to identify malicious users among the thousands of connected clients; therefore, by leveraging the benefits of FL, the clients' data is preserved safely with their own model, and data leakage is prevented to avoid any malicious data modification in the system.

“As the linking sectors are vast in this era, and data production is also vast, it is difficult to identify malicious users among the thousands of connected clients; therefore, by leveraging the benefits of FL, the clients’ data is preserved safely with their own model, and data leakage is prevented to avoid any malicious data modification in the system.”

• AssumptionRests on an assumption: The linking sectors are vast and data production is vast, making it difficult to identify malicious users among thousands of connected clients.

Probably not a substantial claim: may not pass S3 (load-bearing).

Some industrialized nations that have been adopting health-related activities have consistently prioritized eHealth.

“Some industrialized nations that have been adopting health-related activities have consistently prioritized eHealth [150].”

• cites [150] Zaman, S.B.: Importance of learning the public health leadership. Public Health of Indonesia 3(1), 1–3 (2017) sourceNot checked yet

  Not checked in the free run.

May combine two claims.

Medicine resistance or therapy against various diseases, as well as survival rates or explanations, can be examined with strong privacy towards sensitive data.

“Medicine resistance or therapy against various diseases, as well as survival rates or explanations, can be examined with strong privacy towards sensitive data [165].”

• cites [165] Liu, Y., James, J., Kang, J., Niyato, D., Zhang, S.: Privacy-preserving traffic flow prediction: A federated learning approach. IEEE Internet of Things J. 7(8), 7751–7763 (2020) sourceNot checked yet

  Not checked in the free run.

Classified as probably not a substantial claim.

Federated Learning, a distributed collaborative AI model, is specifically appealing for intelligent healthcare since it allows different clients (for example, hospitals) to collaborate on AI training without the need to share local data.

“Federated Learning, a distributed collaborative AI model, is specifically appealing for intelligent healthcare since it allows different clients (for example, hospitals) to collaborate on AI training without the need to share local data.”

Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).

In the world of healthcare, the word artificial intelligence plays a very important and critical function in interpreting the human thinking process through the computer employing many efficient AI-based algorithms to make the difficult examination easy for healthcare professionals.

“In the world of healthcare, the word artificial intelligence plays a very important and critical function in interpreting the human thinking process through the computer employing many efficient AI-based algorithms to make the difficult examination easy for healthcare professionals [167].”

• cites [167] Muhammad, G., Alhussein, M.: Convergence of artificial intelligence and internet of things in smart healthcare: A case study of voice pathology detection. IEEE Access 9, 89198–89209 (2021) sourceNot checked yet

  Not checked in the free run.

Probably not a substantial claim: may not pass S4 (based).

As a result of the integration of Federated Learning with healthcare data informatics, the analysis of extremely sensitive data has become significantly more efficient.

“As a result of the integration of Federated Learning with healthcare data informatics, the analysis of extremely sensitive data has become significantly more efficient, this integration scenario shown in Fig. 4.”

May combine two claims.

As a result, the data remain secure at their origin, and the network, gains access to the server's training results.

“As a result, the data remain secure at their origin, and the network, gains access to the server’s training results.”

May combine two claims.

Attacks from an intruder can access information sent from the hardware system by directly attacking at the decryption point or by weakening the authentication procedure, such as eavesdropping, reply, MITM, rouge, DoS, and DDoS attacks, and these hamper the network layer of the IoMT.

“Then, some attacks from the intruder can access the information sent from the hardware system by directly attacking at the decryption point or by weakening the authentication procedure such as Eavesdropping attack, reply attack, MITM attack, rouge attack, DoS attack, DDoS attack and these type of attack causes to hamper the network layer of the IoMT [200].”

• cites [200] Papaioannou, M., Karageorgou, M., Mantas, G., Sucasas, V., Essop, I., Rodriguez, J., Lymberopoulos, D.: A survey on security threats and countermeasures in internet of medical things (iomt). Transactions on Emerging Telecommunications Technologies p. e4049 (2020) sourceNot checked yet

  Not checked in the free run.

May combine two claims.

Attacks such as preference, side-channel attack, data duplication using the tag, physical hampering of sensor devices, or sensor tracking may cause issues in the perception layer of IoT-based healthcare.

“Preference, side-channel attack [198], data duplication using the tag, physical hampering of the sensor devices, or sensor tracking are some of the attacks that may cause issues in the perception layer [199].”

• cites [198] Shayshab Azad, K.M., Hossain, N., Islam, M.J., Rahman, A., Kabir, S.: Preventive determination and avoidance of ddos attack with sdn over the iot networks. In: 2021 International Conference on Automation, Control and Mechatronics for Industry 4.0 (ACMI), pp. 1–6 (2021). https://doi.org/10.1109/ACMI53878.2021.9528133 sourceNot checked yet

  Not checked in the free run.

• cites [199] Kumar, R., Tripathi, R.: Towards design and implementation of security and privacy framework for internet of medical things (iomt) by leveraging blockchain and ipfs technology. The Journal of Supercomputing pp. 1–40 (2021) sourceNot checked yet

  Not checked in the free run.

Probably not a substantial claim: may not pass S3 (load-bearing).

When a faulty SQL code is injected into the database, it can jeopardize the system's vital information.

“When a faulty SQL code is injected into the database, it can jeopardize the system’s vital information [201].”

• cites [201] Aman, A.H.M., Hassan, W.H., Sameen, S., Attarbashi, Z.S., Alizadeh, M., Latiff, L.A.: Iomt amid covid-19 pandemic: Application, architecture, technology, and security. J. Net. Comput. Appl. 174, 102886 (2021) sourceNot checked yet

  Not checked in the free run.

Probably not a substantial claim: may not pass S3 (load-bearing).

In IoT-based healthcare, security and privacy are among the most serious topics.

“One of the most serious topics is security, and privacy [197].”

• cites [197] Hatzivasilis, G., Soultatos, O., Ioannidis, S., Verikoukis, C., Demetriou, G., Tsatsoulis, C.: Review of security and privacy for the internet of medical things (iomt). In: 2019 15th international conference on distributed computing in sensor systems (DCOSS), pp. 457–464. IEEE (2019) sourceNot checked yet

  Not checked in the free run.

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

In IoT-based systems, heterogeneity is an issue because sensors are from diverse vendors, so the system's anonymity isn't always guaranteed.

“Another issue in IoT-based systems is heterogeneity. Because the sensors are from diverse vendors, the system’s anonymity isn’t always guaranteed.”

May combine two claims.

In today's healthcare systems, a huge number of inbound and outbound networks must be managed, resulting in a problem with supporting a large number of high-speed wireless systems without causing system speed degradation.

“In today’s healthcare systems, a huge number of inbound and outbound networks must be managed. As a result, there is a problem with supporting a large number of high-speed wireless systems without causing system speed degradation [206].”

• cites [206] Muhammad, G., Hossain, M.S., Kumar, N.: Eeg-based pathology detection for home health monitoring. IEEE J. Selected Areas in Commun. 39(2), 603–610 (2021) sourceNot checked yet

  Not checked in the free run.

May combine two claims.

Due to FL's properties, imbalanced and non-IID information can be handled appropriately, ensuring that the AI system's performance is not limited and that a large device in multiple contexts may be trained with their local data and local model without difficulty.

“Again, due to FL’s properties, imbalanced and non-IID information can be handled appropriately, ensuring that the AI system’s performance is not limited and that a large device in multiple contexts may be trained with their local data and local model without difficulty [211].”

• cites [211] Wang, X., Han, Y., Wang, C., Zhao, Q., Chen, X., Chen, M.: In-edge ai: Intelligentizing mobile edge computing, caching and communication by federated learning. IEEE Network 33(5), 156–165 (2019) sourceNot checked yet

  Not checked in the free run.

May combine two claims.

FL provides a privacy-preserving approach that does not require the transfer of sensitive data from the source, instead training local models with their own data and then sharing results with the global network.

“In this case, FL devised a privacy-preserving approach that does not require the transfer of sensitive data from the source, instead of training local models with their own data and then sharing results with the global network [209].”

• cites [209] Vaid, A., Jaladanki, S.K., Xu, J., Teng, S., Kumar, A., Lee, S., Somani, S., Paranjpe, I., De Freitas, J.K., Wanyan, T., et al.: Federated learning of electronic health records to improve mortality prediction in hospitalized patients with covid-19: Machine learning approach. JMIR medical informatics 9(1), e24207 (2021) sourceNot checked yet

  Not checked in the free run.

Probably not a substantial claim: may not pass S3 (load-bearing).

With the growth in data traffic, the network's bandwidth is challenged.

“With the growth in data traffic, the network’s bandwidth is once again challenged [207].”

• cites [207] Hussain, F., Abbas, S.G., Shah, G.A., Pires, I.M., Fayyaz, U.U., Shahzad, F., Garcia, N.M., Zdravevski, E.: A framework for malicious traffic detection in iot healthcare environment. Sensors 21(9), 3025 (2021) sourceNot checked yet

  Not checked in the free run.

May combine two claims.

The transit of data or sensitive information causes security breaches, which, in turn, causes a confidentiality problem in an IoMT-based system.

“The transit of data or sensitive information causes security breaches, which, in turn, causes a confidentiality problem in an IoMT-based system [205].”

• cites [205] Jeyavel, J., Parameswaran, T., Mannan, J.M., Hariharan, U.: Security vulnerabilities and intelligent solutions for iomt systems. In: Internet of Medical Things, pp. 175–194. Springer (2021) sourceNot checked yet

  Not checked in the free run.

May combine two claims.

FL provides a privacy-preserving method to preserve the security and privacy of the hospital system while also providing the benefits of machine learning models to solve numerous difficulties related to healthcare systems.

“As a result, to preserve the security and privacy of the hospital system, FL provides a privacy-preserving method while also providing the benefits of machine learning models to solve numerous difficulties related to healthcare systems [193, 210].”

• cites [193] Long, G., Shen, T., Tan, Y., Gerrard, L., Clarke, A., Jiang, J.: Federated learning for privacy-preserving open innovation future on digital health. In: Humanity Driven AI, pp. 113–133. Springer (2022) sourceNot checked yet

  Not checked in the free run.

• cites [210] Pokhrel, S.R., Choi, J.: A decentralized federated learning approach for connected autonomous vehicles. In: 2020 IEEE Wireless Communications and Networking Conference Workshops (WCNCW), pp. 1–6. IEEE (2020) sourceNot checked yet

  Not checked in the free run.

May combine two claims.

Existing healthcare solutions necessitate the relocation and merging of data from multiple nodes to provide a long-term solution, and because the data being moved is very sensitive, this data migration creates a confidentiality concern.

“Existing healthcare solutions necessitate the relocation and merging of data from multiple nodes to provide a long-term solution. However, because the data being moved is very sensitive, this data migration creates a confidentiality concern [203].”

• cites [203] Rahman, M., Jahankhani, H.: Security vulnerabilities in existing security mechanisms for iomt and potential solutions for mitigating cyber-attacks. In: Information Security Technologies for Controlling Pandemics, pp. 307–334. Springer (2021) sourceNot checked yet

  Not checked in the free run.

Probably not a substantial claim: may not pass S3 (load-bearing).

Blockchain provides a distributed ledger for vulnerable components.

“On the other Blockchain is a provider of distributed ledger for any vulnerable components [241].”

• cites [241] Gulati, P., Sharma, A., Bhasin, K., Azad, C.: Approaches of blockchain with ai: Challenges & future direction. In: Proceedings of the International Conference on Innovative Computing & Communications (ICICC) (2020) sourceNot checked yet

  Not checked in the free run.

May combine two claims.

Storing or transferring IoT-generated data in a short period is difficult and critical.

“As a result, storing or transferring these data in a short period of time is both difficult and critical [239].”

• cites [239] Zhang, K., Song, X., Zhang, C., Yu, S.: Challenges and future directions of secure federated learning: a survey. Frontiers of computer science 16(5), 1–8 (2022) sourceNot checked yet

  Not checked in the free run.

• AssumptionRests on an assumption: IoT sensors have less memory.
• AssumptionRests on an assumption: IoT sensors generate a massive amount of data every nanosecond.

May combine two claims.

Federated Learning is an advanced machine learning method that requires large datasets.

“However, it is an advanced machine learning method that needs large datasets [233, 236].”

• cites [233] Islam, S., Sara, U., Kawsar, A., Rahman, A., Kundu, D., Dipta, D.D., Karim, A.R., Hasan, M.: Sgbba: An efficient method for prediction system in machine learning using imbalance dataset. International Journal of Advanced Computer Science and Applications 12(3) (2021) sourceNot checked yet

  Not checked in the free run.

• cites [236] Hasan, M., Rahman, A., Karim, M.R., Khan, M.S.I., Islam, M.J.: Normalized approach to find optimal number of topics in latent dirichlet allocation (lda). In: Proceedings of International Conference on Trends in Computational and Cognitive Engineering, pp. 341–354. Springer (2021) sourceNot checked yet

  Not checked in the free run.

Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing). May combine two claims.

Combining AI and blockchain produces a highly protected environment that can continue services to current and future users.

“Combined, they produce a highly protected environment that can continue their services to the current and future users incredibly [242, 243].”

• cites [242] Rajawat, A.S., Bedi, P., Goyal, S., Shaw, R.N., Ghosh, A., Aggarwal, S.: Ai and blockchain for healthcare data security in smart cities. In: AI and IoT for Smart City Applications, pp. 185–198. Springer (2022) sourceNot checked yet

  Not checked in the free run.

• cites [243] Latif, S.A., Wen, F.B.X., Iwendi, C., Li-li, F.W., Mohsin, S.M., Han, Z., Band, S.S.: Ai-empowered, blockchain and sdn integrated security architecture for iot network of cyber physical systems. Computer Communications 181, 274–283 (2022) sourceNot checked yet

  Not checked in the free run.

• AssumptionRests on an assumption: AI applications need a secured platform to continue their activities.

Classified as probably not a substantial claim.

The deep learning model's black-box character hinders its explainability and practical application in medicine.

“The deep learning model’s black-box character, on the other hand, hinders its explainability and practical application in medicine.”

• AssumptionRests on an assumption: Deep learning algorithms are typically black-box models, so there is no acceptable explanation for a given prediction.
• AssumptionRests on an assumption: Explainability is vital in healthcare because a given prediction must be explained to persuade a clinical healthcare practitioner and a patient.

Probably not a substantial claim: may not pass S3 (load-bearing).

Various technologies such as SDN and Blockchain are being utilized in healthcare systems.

“In this regard, people are utilizing various technologies into the health care systems such as SDN, Blockchain, etc [234, 235].”

• cites [234] Rahman, A., Sara, U., Kundu, D., Islam, S., Islam, M.J., Hasan, M., Rahman, Z., Nasir, M.K.: Distb-sdoindustry: Enhancing security in industry 4.0 services based on distributed blockchain through software defined networking-iot enabled architecture. International Journal of Advanced Computer Science and Applications 11(9) (2020) sourceNot checked yet

  Not checked in the free run.

• cites [235] Rahman, A., Islam, M.J., Montieri, A., Nasir, M.K., Reza, M.M., Band, S.S., Pescape, A., Hasan, M., Sookhak, M., Mosavi, A.: Smartblock-sdn: An optimized blockchain-sdn framework for resource management in iot. IEEE Access 9, 28361–28376 (2021) sourceNot checked yet

  Not checked in the free run.

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

FL is a well-suited solution for secure data collection in robot training because it forbids data transfer, ensuring data privacy while maintaining autonomy system efficiency.

“In this regard, FL is a well-suited solution since the key demanding task, data transfer, is forbidden in this scenario, ensuring data privacy while maintaining the autonomy system’s efficiency [244].”

• cites [244] Xianjia, Y., Queralta, J.P., Heikkonen, J., Westerlund, T.: Federated learning in robotic and autonomous systems. arXiv preprint arXiv:2104.10141 (2021) sourceNot checked yet

  Not checked in the free run.

• AssumptionRests on an assumption: Training an agent necessitates the collection of large amounts of data from many sources, making secure data collection vital.

May combine two claims.

Although AI in a federated context can address the concerns described previously, deep learning has an explainability difficulty.

“Although AI in a federated context can address the concerns described previously, deep learning has an explainability difficulty.”

• AssumptionRests on an assumption: Deep learning algorithms are typically black-box models, so there is no acceptable explanation for a given prediction.

Probably not a substantial claim: may not pass S3 (load-bearing).

Security in healthcare systems can be provided using SDN, Blockchain, and NFV.

“So, the security could be given utilizing SDN, Blockchain, NFV, etc [237, 238].”

• cites [237] Rahman, A., Islam, M.J., Karim, M.R., Kundu, D., Kabir, S.: An intelligent vaccine distribution process in covid-19 pandemic through blockchain-sdn framework from bangladesh perspective. In: 2021 International Conference on Electronics, Communications and Information Technology (ICECIT), pp. 1–4 (2021). https://doi.org/10.1109/ICECIT54077.2021.9641303 sourceNot checked yet

  Not checked in the free run.

• cites [238] Rahman, A., Rahman, M., Kundu, D., Karim, M.R., Band, S.S., Sookhak, M.: Study on iot for sars-cov-2 with healthcare: present and future perspective. Mathematical Biosciences and Engineering 18(6), 9697–9726 (2021) sourceNot checked yet

  Not checked in the free run.

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

Utilizing FL in IoT data scenarios avoids data storage, distributes data among multiple clients, and ensures individual data security through local training.

“The benefit of utilizing FL in this scenario is that the data storing mechanism is avoided and distributed among multiple clients, and because the data is trained locally on each client, the security of individual data is ensured [240].”

• cites [240] Ahmed, U., Srivastava, G., Lin, J.C.W.: Reliable customer analysis using federated learning and exploring deep-attention edge intelligence. Future Generation Computer Systems 127, 70–79 (2022) sourceNot checked yet

  Not checked in the free run.

Classified as probably not a substantial claim.

Humans can understand and describe how an AI system reached a decision using Explainable Artificial Intelligence (XAI).

“Humans can understand and describe how an AI system reached a decision using Explainable Artificial Intelligence (XAI) [212].”

• cites [212] Gunning, D., Aha, D.: Darpa’s explainable artificial intelligence (xai) program. AI magazine 40(2), 44–58 (2019) sourceNot checked yet

  Not checked in the free run.

Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).

AI plays a vital role in healthcare by using algorithms to make complex examinations easier for healthcare professionals.

“In healthcare, AI performs a vital and crucial role in understanding the human thinking process through the computer using various effective AI-based algorithms to make complex examinations easier for healthcare professionals [83].”

• cites [83] Xu, J., Glicksberg, B.S., Su, C., Walker, P., Bian, J., Wang, F.: Federated learning for healthcare informatics. J. Healthcare Informat. Res. 5(1), 1–19 (2021) sourceNot checked yet

  Not checked in the free run.

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

Combining blockchain and Federated Learning ensures security of medical data and an efficient model.

“Thus the medical data through the blockchain and Federated Learning security is ensured, and an efficient model can be assured [232].”

• cites [232] Chang, Y., Fang, C., Sun, W.: A blockchain-based federated learning method for smart healthcare. Computational Intelligence and Neuroscience 2021 (2021) sourceNot checked yet

  Not checked in the free run.

• AssumptionRests on an assumption: Medical data is extremely sensitive, and any changes could result in a massive loss for the entire system.

Probably not a substantial claim: may not pass S3 (load-bearing).

In FL-blockchain for Industry 5.0, initial parameters are passed through blockchain blocks for secure transmission.

“The initial parameters then passed through the blocks of the blockchain technology for secure transmission [246, 247].”

• cites [246] Xu, L., Zhou, X., Tao, Y., Yu, X., Yu, M., Khan, F.: Af relaying secrecy performance prediction for 6g mobile communication networks in industry 5.0. IEEE Transactions on Industrial Informatics (2021) sourceNot checked yet

  Not checked in the free run.

• cites [247] Chander, B., Pal, S., De, D., Buyya, R.: Artificial intelligence-based internet of things for industry 5.0. In: Artificial Intelligence-based Internet of Things Systems, pp. 3–45. Springer (2022) sourceNot checked yet

  Not checked in the free run.

May combine two claims.

FL ensures data privacy and eliminates the need to transmit sensitive user data from one location to another.

“As a result, data privacy is also ensured, and there is no need to transmit sensitive user data from one location to another [245].”

• cites [245] Xu, W., Fang, W., Ding, Y., Zou, M., Xiong, N.: Accelerating federated learning for iot in big data analytics with pruning, quantization and selective updating. IEEE Access 9, 38457–38466 (2021) sourceNot checked yet

  Not checked in the free run.