Slide
Centre Interdisciplinaire
de Recherche et d’Innovation
en Cybersécurité et Société
de Recherche et d’Innovation
en Cybersécurité et Société
1.
Elhajjout, A.; Houda, Z. A. E.; Moudoud, H.; Brik, B.; Jan, M. A.
Federated Large Language Models for A Trustworthy and Privacy-Preserving Healthcare: Applications, Challenges, and Future Research Directions Article de journal
Dans: IEEE Journal of Biomedical and Health Informatics, 2026, ISSN: 21682194 (ISSN).
Résumé | Liens | BibTeX | Étiquettes: 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
@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}
}
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.
2.
Moudoud, H.; Houda, Z. Abou El; Brik, B.
Advancing Privacy and Fairness in Healthcare Using Federated Edge Learning and Blockchain Article de journal
Dans: IEEE Internet of Things Journal, vol. 12, no 22, p. 46129–46137, 2025, ISSN: 23274662 (ISSN).
Résumé | Liens | BibTeX | Étiquettes: 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
@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}
}
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.