Integrating federated learning with IoMT for managing obesity in smart city

Siddiqui, Shama; Khan, Anwar Ahmed; Dev, Kapal; Dey, Indrakshi

doi:10.1145/3477084.3484950

Cited by 5 publications

(5 citation statements)

References 9 publications

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“…Gong et al [ 22 ], experimentally evaluated their proposed collaborative learning scheme on diabetes data set and demonstrated its practicality for mHealth monitoring scenarios. Siddiqui et al [ 23 ] integrated FL with the Internet of Medical Things architecture to detect the risk of obesity in individuals. BMI data were analyzed to assess the obesity risk, and expert recommendations were generated based on the results.…”

Section: Resultsmentioning

confidence: 99%

“…Six of the studies considered the use of real-time data in mHealth. For example, 3 of these studies [ 9 , 21 , 23 ] developed methods that can continuously adapt to new emerging data by repeating the entire process with the accumulation of new data. Several studies developed specific methods or used unique data sources to address this issue.…”

Section: Resultsmentioning

confidence: 99%

“…Remote monitoring a [4,12,13,[30][31][32][33][34][35][36][37][38] Activities of daily living [12,13] Parkinson disease [15] Epilepsy [22] Diabetes [19][20][21] Cardiac health [23] Obesity [16][17][18] Stress Diagnostic and treatment support b [9] Depression [26,27] Dermatological disease [28] Cancer [29] Perioperative complications a Remote monitoring allows individuals to self-monitor their health status and manage the progression of chronic diseases from a remote location, such as their own home. b Diagnostic and treatment support refers to the provision of assistance for the diagnosis and treatment of patients from a remote location.…”

Section: Studies Areas and Applicationsmentioning

confidence: 99%

“…Privacy leakage [9,21,23,35,36,38] Sensing measurements are generated continuously, and the FL process should be working continuously with new emerging data.…”

Section: Referencesmentioning

confidence: 99%

“…Statistical heterogeneity [4] Meta-learning [12,18,21,30,36] Model personalization [34,35,37] User clustering [16,31,38] Adaptive update scheme Expensive communication [4,9,12,13,20,26,27,30,36] FedAvg [15,37,38] Flexible local updates [21,28,32,34,35] Compression schemes System heterogeneity [16] Active sampling [34,35] Fault tolerance Privacy leakage [28,38] Differential privacy [12,15,22,37] Homomorphic encryption [16,21,29] Strict information sharing scheme [33] 2-stage privacy-preserving scheme Real-time data stream [36] Incremental learning [38] Web-based learning [9,21,23] Repeat working with continuously emerging data…”

Section: References Challenges and Solutionsmentioning

confidence: 99%

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Applications of Federated Learning in Mobile Health: Scoping Review

Wang¹,

Du²,

Gong³

et al. 2023

J Med Internet Res

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Background The proliferation of mobile health (mHealth) applications is partly driven by the advancements in sensing and communication technologies, as well as the integration of artificial intelligence techniques. Data collected from mHealth applications, for example, on sensor devices carried by patients, can be mined and analyzed using artificial intelligence–based solutions to facilitate remote and (near) real-time decision-making in health care settings. However, such data often sit in data silos, and patients are often concerned about the privacy implications of sharing their raw data. Federated learning (FL) is a potential solution, as it allows multiple data owners to collaboratively train a machine learning model without requiring access to each other’s raw data. Objective The goal of this scoping review is to gain an understanding of FL and its potential in dealing with sensitive and heterogeneous data in mHealth applications. Through this review, various stakeholders, such as health care providers, practitioners, and policy makers, can gain insight into the limitations and challenges associated with using FL in mHealth and make informed decisions when considering implementing FL-based solutions. Methods We conducted a scoping review following the guidelines of PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews). We searched 7 commonly used databases. The included studies were analyzed and summarized to identify the possible real-world applications and associated challenges of using FL in mHealth settings. Results A total of 1095 articles were retrieved during the database search, and 26 articles that met the inclusion criteria were included in the review. The analysis of these articles revealed 2 main application areas for FL in mHealth, that is, remote monitoring and diagnostic and treatment support. More specifically, FL was found to be commonly used for monitoring self-care ability, health status, and disease progression, as well as in diagnosis and treatment support of diseases. The review also identified several challenges (eg, expensive communication, statistical heterogeneity, and system heterogeneity) and potential solutions (eg, compression schemes, model personalization, and active sampling). Conclusions This scoping review has highlighted the potential of FL as a privacy-preserving approach in mHealth applications and identified the technical limitations associated with its use. The challenges and opportunities outlined in this review can inform the research agenda for future studies in this field, to overcome these limitations and further advance the use of FL in mHealth.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Studies Areas and Applicationsmentioning

confidence: 99%

“…Privacy leakage [9,21,23,35,36,38] Sensing measurements are generated continuously, and the FL process should be working continuously with new emerging data.…”

Section: Referencesmentioning

confidence: 99%

Section: References Challenges and Solutionsmentioning

confidence: 99%

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Applications of Federated Learning in Mobile Health: Scoping Review

Wang¹,

Du²,

Gong³

et al. 2023

J Med Internet Res

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Blockchain-based federated learning in internet of health things

Akoramurthy,

Surendiran,

Dhivya

et al. 2024

Federated Learning for Digital Healthcare Systems

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Applications of Federated Learning in Mobile Health: Scoping Review (Preprint)

Wang¹,

Guo²,

Choo³

2022

Preprint

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BACKGROUND Advances in sensing and other communication technologies, as well as artificial intelligence (AI), have driven the widespread use of mobile health (mHealth) applications. For example, data collected from sensor devices carried by patients can be mined and analyzed using AI-based solutions to facilitate remote and (near) real-time decision-making in healthcare settings. However, patients may not wish to share their raw data due to privacy concerns. One possible solution is to utilize federated learning (FL) where only the trained parameters (and not the raw data) are shared during the model training process. OBJECTIVE This scoping literature review explains what federated learning is, and how it can be used to deal with sensitive and heterogeneous data in mHealth applications so that it can help healthcare practitioners understand the associated limitations and challenges of FL in mHealth. METHODS A scoping review was conducted following the guidelines of PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) [1]. We searched seven (7) commonly used databases. The included studies were analyzed and summarized to identify possible real-world applications and associated challenges of applying FL in healthcare settings. A total of 1,095 articles were retrieved during the database search, and 26 articles that met the inclusion criteria were eventually included in the review. RESULTS Common applications of FL in mHealth include monitoring self-care ability, monitoring mental health status, disease progression, and auxiliary diagnosis of disease. Based on the analysis, we identified a number of research challenges and opportunities, such as those relating to communication costs, statistical heterogeneity, and privacy leakage. CONCLUSIONS While FL is a viable approach to addressing privacy concerns in mHealth applications, there are a number of technical limitations associated with the use of FL as outlined in this article. Hopefully, the challenges and opportunities identified in this work will inform future research agenda on this topic.

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Integrating federated learning with IoMT for managing obesity in smart city

Cited by 5 publications

References 9 publications

Applications of Federated Learning in Mobile Health: Scoping Review

Applications of Federated Learning in Mobile Health: Scoping Review

Blockchain-based federated learning in internet of health things

Applications of Federated Learning in Mobile Health: Scoping Review (Preprint)

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