@article{elhajjoutFederatedLargeLanguage2026,

title = {Federated Large Language Models for A Trustworthy and Privacy-Preserving Healthcare: Applications, Challenges, and Future Research Directions},

author = {A. Elhajjout and Z. A. E. Houda and H. Moudoud and B. Brik and M. A. Jan},

url = {https://www.scopus.com/pages/publications/105034640126?origin=resultslist},

doi = {10.1109/JBHI.2026.3679612},

issn = {21682194 (ISSN)},

year  = {2026},

date = {2026-01-01},

journal = {IEEE Journal of Biomedical and Health Informatics},

publisher = {Institute of Electrical and Electronics Engineers Inc.},

abstract = {Large-scale foundation models, especially Federated Large Language Models (FLLMs), aim to transform digital health by enabling clinically-grade natural language processing while keeping sensitive data local. However, their adoption is hindered by two main issues: (i) the computational and communication burden of parameter-rich models on resource-constrained Internet-of-Medical-Things (IoMT) devices, and (ii) performance degradation caused by Non-Independent and Identically Distributed (Non-IID) patient data. This paper presents a comprehensive survey of Federated Learning (FL) for LLMs in Healthcare (FedMed-LLMs). We review the foundations of FL and medical LLMs. Then, we present the FL-enabled LLMs applications in healthcare, and we examine their issues in terms of privacy, robustness, and trustworthiness. Finally, we present a set of core research problems and a comprehensive research agenda that identifies future directions for building robust and scalable FedMed-LLMs systems. © 2013 IEEE.},

keywords = {Application research, Artificial intelligence, digital health, Distributed computer systems, Federated learning, Fine tuning, Health care, Health care application, Healthcare AI, human, Internet, Internet of medical thing, Internet of Medical Things, Language model, Large language model, large language models, Learning systems, male, Medical computing, natural language processing, Parameter-efficient fine-tuning, patient coding, Privacy, Privacy preservation, Privacy preserving, Privacy-preserving techniques, review, Sensitive data, trustworthiness},

pubstate = {published},

tppubtype = {article}

}

@article{moudoudAdvancingPrivacyFairness2025,

title = {Advancing Privacy and Fairness in Healthcare Using Federated Edge Learning and Blockchain},

author = {H. Moudoud and Z. Abou El Houda and B. Brik},

url = {https://www.scopus.com/pages/publications/105012264671?origin=resultslist},

doi = {10.1109/JIOT.2025.3589179},

issn = {23274662 (ISSN)},

year  = {2025},

date = {2025-01-01},

journal = {IEEE Internet of Things Journal},

volume = {12},

number = {22},

pages = {46129–46137},

publisher = {Institute of Electrical and Electronics Engineers Inc.},

abstract = {artificial intelligence (AI) has revolutionized many fields, including healthcare. The adoption of AI techniques in critical healthcare tasks, such as cancer diagnosis, holds great promise for revolutionizing the healthcare system. AI algorithms can be trained on vast datasets to recognize patterns, detect anomalies, and provide accurate assessments. However, the lack of realistic and up-to-date medical data poses a significant challenge to the widespread adoption of AI techniques. Additionally, privacy concerns surrounding sensitive medical data, particularly patient health records (PHRs), hinder data sharing among healthcare practitioners. This article aims to address these challenges by proposing a novel framework, entitled SecureMed, that uses federated learning (FL) and Blockchain to preserve privacy in the healthcare system. In particular, SecureMed consists of: 1) a novel distributed architecture that enables secure collaboration among multiple mobile edge computing (MEC)-based Internet of Medical Things (IoMT) devices, while ensuring the privacy of healthcare systems; 2) a fairness-aware FL solution to ensure that model performance is balanced across all participating healthcare institutions, addressing the issue of imbalanced data contributions; 3) a secure multiparty computation (SMPC) protocol to ensure secure aggregation of local model updates; and 4) a blockchain-based reputation model for collaborative FL training. The proposed framework leverages smart contracts to ensure trustworthiness, decentralization, and transparency in the FL process. The experimental results using the CIC IoMT dataset 2024 highlight the promising potential of SecureMed in revolutionizing healthcare systems. © 2014 IEEE.},

keywords = {Artificial intelligence algorithms, Artificial intelligence techniques, Block-chain, Blockchain, cancer diagnosis, Diagnosis, Distributed computer systems, Federated edge learning, Health care, Healthcare, Healthcare systems, Internet of medical thing, Internet of Medical Things (IoMT), Learning systems, Medical computing, Medical data, Network security, Privacy, Sensitive data},

pubstate = {published},

tppubtype = {article}

}

@article{falkInternetSensesHealthcare2025,

title = {The Internet of Senses for Healthcare 5.0: Enabling Technologies, Opportunities, and Challenges},

author = {T. H. Falk},

url = {https://www.scopus.com/pages/publications/105023337478?origin=resultslist},

doi = {10.1007/978-981-96-1437-0_2},

issn = {978-981961437-0 (ISBN); 978-981961436-3 (ISBN)},

year  = {2025},

date = {2025-01-01},

journal = {Healthcare in the Digital Age: Perspectives for Sustainable Innovation and Assessment},

pages = {11–29},

publisher = {Springer Science+Business Media},

abstract = {Multisensory virtual reality immerses users in a virtual setting by stimulating all their senses. Coupled with artificial intelligence, next-generation communications, and human sensing technologies, the so-called Internet of Senses (IoS) will drive healthcare 5.0 applications for the betterment of humankind. Multisensory nature exposure experiences have already shown to reduce symptoms of anxiety. The integration of haptic technologies with olfactory stimuli could revolutionise remote medical applications, bringing specialised care to remote communities. Having this said, while the number of applications is numerous, we need to stop and think about the challenges that will arise, such as the environmental impact of developing and running these tools. Moreover, while these tools can increase accessibility to those that may have, for example, mobility issues, it can, on the other hand, limit usage for those with limited financial resources needed to run these tools. As such, do-it-yourself and/or open-source tools will be crucial to ensure equitable access worldwide. In this chapter, a description is given of the work done (and ongoing) by the author and his collaborators on how to enable healthcare 5.0 applications based on the IoS, how to better democratise them, and make them safer for users and the planet. © 2025 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.},

keywords = {Artificial intelligence, Enabling technologies, Environmental impact, Health care, Healthcare 5.0, Human sensing, Immersive, Medical applications, Medical informatics, Multisensory, Multisensory Immersive Reality, Olfaction, Sustainability, sustainable development, Technology challenges, Technology opportunities, User experience, User interfaces, Users' experiences, virtual reality},

pubstate = {published},

tppubtype = {article}

}

@article{bouchard_delivering_2004,

title = {Delivering Cognitive-Behavior Therapy for Panic Disorder with Agoraphobia in Videoconference},

author = {S. Bouchard and B. Paquin and R. Payeur and M. Allard and V. Rivard and T. Fournier and P. Renaud and J. Lapierre},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-1842783565&doi=10.1089%2f153056204773644535&partnerID=40&md5=b16d49e06152cafecd086aa08c3326b7},

doi = {10.1089/153056204773644535},

issn = {15305627 (ISSN)},

year  = {2004},

date = {2004-01-01},

journal = {Telemedicine and e-Health},

volume = {10},

number = {1},

pages = {13–25},

publisher = {Mary Ann Liebert Inc.},

abstract = {Delivering psychotherapy by videoconference could significantly increase the accessibility of empirically validated treatments. The aim of this study was to compare the effectiveness of cognitive-behavior therapy (CBT) for panic disorder with agoraphobia (PDA) when the therapy is delivered either face-to-face or by videoconference. A sample of 21 participants was treated either face-to-face or by videoconference. Results showed that CBT delivered by videoconference was as effective as CBT delivered face-to-face. There was a statistically significant reduction in all measures, and the number of panic-free participants among those receiving CBT by videoconference was 81% at post-treatment and 91% at the 6-month follow-up. None of the comparisons with face-to-face psychotherapy suggested that CBT delivered by videoconference was less effective. These results were confirmed by analyses of effect size. The participants reported the development of an excellent therapeutic alliance in videoconference as early as the first therapy session. The importance of these results for treatment accessibility is discussed. Hypotheses are proposed to explain the rapid creation of strong therapeutic alliances in videoconferencing.},

note = {Publisher: Mary Ann Liebert Inc.},

keywords = {adult, agoraphobia, article, behavior therapy, clinical article, Cognitive systems, Cognitive-behavior therapy (CBT), female, Health care, health care delivery, human, male, Medical problems, panic, Patient monitoring, priority journal, psychotherapy, telecommunication, Telemedicine, Therapeutic alliances, validation process, Video conferencing, videorecording},

pubstate = {published},

tppubtype = {article}

}

@article{bouchardDeliveringCognitiveBehaviorTherapy2004,

title = {Delivering Cognitive-Behavior Therapy for Panic Disorder with Agoraphobia in Videoconference},

author = {S. Bouchard and B. Paquin and R. Payeur and M. Allard and V. Rivard and T. Fournier and P. Renaud and J. Lapierre},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-1842783565&doi=10.1089%2f153056204773644535&partnerID=40&md5=b16d49e06152cafecd086aa08c3326b7},

doi = {10.1089/153056204773644535},

issn = {15305627 (ISSN)},

year  = {2004},

date = {2004-01-01},

journal = {Telemedicine and e-Health},

volume = {10},

number = {1},

pages = {13–25},

publisher = {Mary Ann Liebert Inc.},

abstract = {Delivering psychotherapy by videoconference could significantly increase the accessibility of empirically validated treatments. The aim of this study was to compare the effectiveness of cognitive-behavior therapy (CBT) for panic disorder with agoraphobia (PDA) when the therapy is delivered either face-to-face or by videoconference. A sample of 21 participants was treated either face-to-face or by videoconference. Results showed that CBT delivered by videoconference was as effective as CBT delivered face-to-face. There was a statistically significant reduction in all measures, and the number of panic-free participants among those receiving CBT by videoconference was 81% at post-treatment and 91% at the 6-month follow-up. None of the comparisons with face-to-face psychotherapy suggested that CBT delivered by videoconference was less effective. These results were confirmed by analyses of effect size. The participants reported the development of an excellent therapeutic alliance in videoconference as early as the first therapy session. The importance of these results for treatment accessibility is discussed. Hypotheses are proposed to explain the rapid creation of strong therapeutic alliances in videoconferencing.},

keywords = {adult, agoraphobia, article, behavior therapy, clinical article, Cognitive systems, Cognitive-behavior therapy (CBT), female, Health care, health care delivery, human, male, Medical problems, panic, Patient monitoring, priority journal, psychotherapy, telecommunication, Telemedicine, Therapeutic alliances, validation process, Video conferencing, videorecording},

pubstate = {published},

tppubtype = {article}

}