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 Journal Article
In: IEEE Journal of Biomedical and Health Informatics, 2026, ISSN: 21682194 (ISSN).
Abstract | Links | BibTeX | Tags: 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.
Mehrban, A.; Moudoud, H.; Brik, B.; Houda, Z. A. El
Enhancing Spectral Efficiency and Resilience in SAGIN-ITS: An IRS-Assisted Semantic Offloading Framework Journal Article
In: IEEE Internet of Things Magazine, vol. 9, no. 4, pp. 30–36, 2026, ISSN: 25763180 (ISSN).
Abstract | Links | BibTeX | Tags: Air grounds, Bandwidth, Computation offloading, Federated learning, federated learning (FL), IEEE Standards, Integrated networks, Intelligent reflecting surface, Intelligent reflecting surfaces (IRS), Intelligent transportation system, Intelligent transportation systems, intelligent transportation systems (ITS), Intelligent vehicle highway systems, Learning systems, Reflecting surface, Semantic offloading, Semantics, Sensor networks, Sensors data, Signal encoding, Space-air-ground integrated network, space-air-ground integrated networks (SAGIN), Traffic congestion, Transmissions
@article{mehrbanEnhancingSpectralEfficiency2026,
title = {Enhancing Spectral Efficiency and Resilience in SAGIN-ITS: An IRS-Assisted Semantic Offloading Framework},
author = {A. Mehrban and H. Moudoud and B. Brik and Z. A. El Houda},
url = {https://www.scopus.com/pages/publications/105036681552?origin=resultslist},
doi = {10.1109/MIOT.2026.3676349},
issn = {25763180 (ISSN)},
year = {2026},
date = {2026-01-01},
journal = {IEEE Internet of Things Magazine},
volume = {9},
number = {4},
pages = {30–36},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {In dense Vehicle-to-Everything (V2X) scenarios within Space-Air-Ground Integrated Networks (SAGIN), connected autonomous cars (CAVs) generate a large volume of sensor data, which leads to significant bandwidth congestion. There is a risk to safety because of the poor Packet Success Rate (PSR) and dangerous transmission delays caused by this congestion. To address this issue, we introduce SemantIRS, a framework that integrates semantic offloading with Intelligent Reflecting Surface (IRS)-assisted links to achieve high network resilience, when compute resources permit and lower latency. SemantIRS deploys lightweight Large Language Models (LLMs) as encoders to compress massive sensor data at the edge of the SAGIN network before transmission through IRS-assisted links. Intent-Based Networking (IBN) and Federated Learning (FL) are also integrated to automate resilience policies and preserve distributed offloading privacy throughout SAGIN layers, respectively. We conduct our experiments using a 12-state hardware benchmark of three LLM encoders (Phi-2, Llama-2-7B-Chat, and Mistral-7B-Instruct-v0.2) on four compute tiers (TPU v6e-1, A100, L4, and Intel Xeon CPU). The measured inference latencies are then fed into an IEEE 802.11p airtime simulator. Our obtained results show that, in congested network conditions, SemantIRS improves the packet success rate from 1.7% to 66.4% while reducing transmission energy by 90%. © 2026 IEEE.},
keywords = {Air grounds, Bandwidth, Computation offloading, Federated learning, federated learning (FL), IEEE Standards, Integrated networks, Intelligent reflecting surface, Intelligent reflecting surfaces (IRS), Intelligent transportation system, Intelligent transportation systems, intelligent transportation systems (ITS), Intelligent vehicle highway systems, Learning systems, Reflecting surface, Semantic offloading, Semantics, Sensor networks, Sensors data, Signal encoding, Space-air-ground integrated network, space-air-ground integrated networks (SAGIN), Traffic congestion, Transmissions},
pubstate = {published},
tppubtype = {article}
}
In dense Vehicle-to-Everything (V2X) scenarios within Space-Air-Ground Integrated Networks (SAGIN), connected autonomous cars (CAVs) generate a large volume of sensor data, which leads to significant bandwidth congestion. There is a risk to safety because of the poor Packet Success Rate (PSR) and dangerous transmission delays caused by this congestion. To address this issue, we introduce SemantIRS, a framework that integrates semantic offloading with Intelligent Reflecting Surface (IRS)-assisted links to achieve high network resilience, when compute resources permit and lower latency. SemantIRS deploys lightweight Large Language Models (LLMs) as encoders to compress massive sensor data at the edge of the SAGIN network before transmission through IRS-assisted links. Intent-Based Networking (IBN) and Federated Learning (FL) are also integrated to automate resilience policies and preserve distributed offloading privacy throughout SAGIN layers, respectively. We conduct our experiments using a 12-state hardware benchmark of three LLM encoders (Phi-2, Llama-2-7B-Chat, and Mistral-7B-Instruct-v0.2) on four compute tiers (TPU v6e-1, A100, L4, and Intel Xeon CPU). The measured inference latencies are then fed into an IEEE 802.11p airtime simulator. Our obtained results show that, in congested network conditions, SemantIRS improves the packet success rate from 1.7% to 66.4% while reducing transmission energy by 90%. © 2026 IEEE.
3.
Selamnia, A.; Moudoud, H.; Khoukhi, L.; Brik, B.; Houda, Z. A. El
QSFL-ID: Quantum-Split Federated Learning for Intrusion Detection in IIoT Networks Proceedings Article
In: IEEE Int Conf Commun, Institute of Electrical and Electronics Engineers Inc., 2026, ISBN: 15503607 (ISSN); 979-831954209-0 (ISBN), (Journal Abbreviation: IEEE Int Conf Commun).
Abstract | Links | BibTeX | Tags: Automation, Complex networks, Cyber threats, Federated learning, IIoT, Industrial automation, Industrial internet of thing, Intrusion Detection, Intrusion-Detection, Learning systems, Machine learning methods, Network intrusion, Network security, Privacy-preserving techniques, Processing power, QML, Quantum circuit, Quantum entanglement, Split Learning, Variational quantum circuit, VQC
@inproceedings{selamniaQSFLIDQuantumSplitFederated2026,
title = {QSFL-ID: Quantum-Split Federated Learning for Intrusion Detection in IIoT Networks},
author = {A. Selamnia and H. Moudoud and L. Khoukhi and B. Brik and Z. A. El Houda},
url = {https://www.scopus.com/pages/publications/105045419288?origin=resultslist},
doi = {10.1109/ICC59461.2026.11587037},
isbn = {15503607 (ISSN); 979-831954209-0 (ISBN)},
year = {2026},
date = {2026-01-01},
booktitle = {IEEE Int Conf Commun},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {The Industrial Internet of Things (IIoT) is reshaping industrial automation through interconnected, intelligent systems. However, this evolution increases exposure to sophisticated cyber threats, especially given the constraints of IIoT devices such as limited processing power, bandwidth, and heterogeneous protocols. Traditional machine learning methods often fail to meet these security demands due to their computational intensity and centralized data requirements. To address this, we propose a hybrid quantum-classical Split Federated Learning (SFL) framework for intrusion detection in IIoT networks. Our method integrates Variational Quantum Circuits (VQCs) to model complex, non-linear data relationships, enhancing detection accuracy while preserving data privacy through decentralized learning. The architecture assigns lightweight preprocessing to edge devices and complex analysis to a quantum backend, ensuring efficiency and scalability. To evaluate the proposed framework, we conduct extensive experiments on the real-world EDGE-IIoT dataset; the experimental results demonstrate that the model attains 95.5% training accuracy, significantly surpassing classical SFL (85.7%). In addition, the quantum model's enhanced entanglement properties and expressibility strengthen its generalization performance. This approach offers an efficient and privacy-preserving solution for securing IIoT systems. © 2026 IEEE.},
note = {Journal Abbreviation: IEEE Int Conf Commun},
keywords = {Automation, Complex networks, Cyber threats, Federated learning, IIoT, Industrial automation, Industrial internet of thing, Intrusion Detection, Intrusion-Detection, Learning systems, Machine learning methods, Network intrusion, Network security, Privacy-preserving techniques, Processing power, QML, Quantum circuit, Quantum entanglement, Split Learning, Variational quantum circuit, VQC},
pubstate = {published},
tppubtype = {inproceedings}
}
The Industrial Internet of Things (IIoT) is reshaping industrial automation through interconnected, intelligent systems. However, this evolution increases exposure to sophisticated cyber threats, especially given the constraints of IIoT devices such as limited processing power, bandwidth, and heterogeneous protocols. Traditional machine learning methods often fail to meet these security demands due to their computational intensity and centralized data requirements. To address this, we propose a hybrid quantum-classical Split Federated Learning (SFL) framework for intrusion detection in IIoT networks. Our method integrates Variational Quantum Circuits (VQCs) to model complex, non-linear data relationships, enhancing detection accuracy while preserving data privacy through decentralized learning. The architecture assigns lightweight preprocessing to edge devices and complex analysis to a quantum backend, ensuring efficiency and scalability. To evaluate the proposed framework, we conduct extensive experiments on the real-world EDGE-IIoT dataset; the experimental results demonstrate that the model attains 95.5% training accuracy, significantly surpassing classical SFL (85.7%). In addition, the quantum model's enhanced entanglement properties and expressibility strengthen its generalization performance. This approach offers an efficient and privacy-preserving solution for securing IIoT systems. © 2026 IEEE.
4.
Valem, L. P.; Pedronette, D. C. G.; Allili, M. S.
Contrastive Loss Based on Contextual Similarity for Image Classification Proceedings Article
In: G., Bebis; V., Patel; J., Gu; J., Panetta; Y., Gingold; K., Johnsen; M.S., Arefin; S., Dutta; A., Biswas (Ed.): Lect. Notes Comput. Sci., pp. 58–69, Springer Science and Business Media Deutschland GmbH, 2025, ISBN: 03029743 (ISSN); 978-303177391-4 (ISBN), (Journal Abbreviation: Lect. Notes Comput. Sci.).
Abstract | Links | BibTeX | Tags: Adversarial machine learning, Classification accuracy, Contrastive Learning, Cross entropy, Experimental evaluation, Federated learning, Image classification, Image comparison, Image embedding, Images classification, Model generalization, Model robustness, Neighborhood information, Self-supervised learning, Similarity measure
@inproceedings{valem_contrastive_2025,
title = {Contrastive Loss Based on Contextual Similarity for Image Classification},
author = {L. P. Valem and D. C. G. Pedronette and M. S. Allili},
editor = {Bebis G. and Patel V. and Gu J. and Panetta J. and Gingold Y. and Johnsen K. and Arefin M.S. and Dutta S. and Biswas A.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85218461565&doi=10.1007%2f978-3-031-77392-1_5&partnerID=40&md5=cf885303646c3b1a4f4eacb87d02a2b6},
doi = {10.1007/978-3-031-77392-1_5},
isbn = {03029743 (ISSN); 978-303177391-4 (ISBN)},
year = {2025},
date = {2025-01-01},
booktitle = {Lect. Notes Comput. Sci.},
volume = {15046 LNCS},
pages = {58–69},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {Contrastive learning has been extensively exploited in self-supervised and supervised learning due to its effectiveness in learning representations that distinguish between similar and dissimilar images. It offers a robust alternative to cross-entropy by yielding more semantically meaningful image embeddings. However, most contrastive losses rely on pairwise measures to assess the similarity between elements, ignoring more general neighborhood information that can be leveraged to enhance model robustness and generalization. In this paper, we propose the Contextual Contrastive Loss (CCL) to replace pairwise image comparison by introducing a new contextual similarity measure using neighboring elements. The CCL yields a more semantically meaningful image embedding ensuring better separability of classes in the latent space. Experimental evaluation on three datasets (Food101, MiniImageNet, and CIFAR-100) has shown that CCL yields superior results by achieving up to 10.76% relative gains in classification accuracy, particularly for fewer training epochs and limited training data. This demonstrates the potential of our approach, especially in resource-constrained scenarios. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.},
note = {Journal Abbreviation: Lect. Notes Comput. Sci.},
keywords = {Adversarial machine learning, Classification accuracy, Contrastive Learning, Cross entropy, Experimental evaluation, Federated learning, Image classification, Image comparison, Image embedding, Images classification, Model generalization, Model robustness, Neighborhood information, Self-supervised learning, Similarity measure},
pubstate = {published},
tppubtype = {inproceedings}
}
Contrastive learning has been extensively exploited in self-supervised and supervised learning due to its effectiveness in learning representations that distinguish between similar and dissimilar images. It offers a robust alternative to cross-entropy by yielding more semantically meaningful image embeddings. However, most contrastive losses rely on pairwise measures to assess the similarity between elements, ignoring more general neighborhood information that can be leveraged to enhance model robustness and generalization. In this paper, we propose the Contextual Contrastive Loss (CCL) to replace pairwise image comparison by introducing a new contextual similarity measure using neighboring elements. The CCL yields a more semantically meaningful image embedding ensuring better separability of classes in the latent space. Experimental evaluation on three datasets (Food101, MiniImageNet, and CIFAR-100) has shown that CCL yields superior results by achieving up to 10.76% relative gains in classification accuracy, particularly for fewer training epochs and limited training data. This demonstrates the potential of our approach, especially in resource-constrained scenarios. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
5.
Amari, H.; Houda, Z. A. El; Moudoud, H.; Khoukhi, L.; Belguith, L. H.
Blockchain-Based Federated Learning for Enhanced Cyber-Threats Detection in Connected Vehicles Proceedings Article
In: M., Valenti; D., Reed; M., Torres (Ed.): IEEE Int Conf Commun, pp. 4257–4262, Institute of Electrical and Electronics Engineers Inc., 2025, ISBN: 15503607 (ISSN); 979-833150521-9 (ISBN), (Journal Abbreviation: IEEE Int Conf Commun).
Abstract | Links | BibTeX | Tags: Block-chain, Blockchain, Central layers, Computer crime, Connected vehicle, Connected Vehicles, Cyber threats, Cyberthreat detection, Cyberthreats, Cyberthreats Detection, Data privacy, Federated learning, Intelligent transport, Intelligent vehicle highway systems, Internet of things, Intrusion Detection, Intrusion Detection Systems, Learning systems, Network security, SDN, Threat detection, Traffic control, Vehicles
@inproceedings{amariBlockchainBasedFederatedLearning2025,
title = {Blockchain-Based Federated Learning for Enhanced Cyber-Threats Detection in Connected Vehicles},
author = {H. Amari and Z. A. El Houda and H. Moudoud and L. Khoukhi and L. H. Belguith},
editor = {Valenti M. and Reed D. and Torres M.},
url = {https://www.scopus.com/pages/publications/105018456633?origin=resultslist},
doi = {10.1109/ICC52391.2025.11161266},
isbn = {15503607 (ISSN); 979-833150521-9 (ISBN)},
year = {2025},
date = {2025-01-01},
booktitle = {IEEE Int Conf Commun},
pages = {4257–4262},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Over the past few years, there have been made significant strides in advancing the Internet of Vehicles (IoV), recognizing its strategic importance in Intelligent Transport Systems. The proliferation of connected and autonomous vehicles on the roads has propelled the IoV into the spotlight. However, addressing the specific demands of vehicular networks, such as low latency, high mobility, extensive connectivity of 5G/6G networks, and robust security, remains a substantial challenge. Therefore, there is a critical need for substantial progress in implementing a resilient Intrusion Detection System within the IoV ecosystem. This paper introduces VFed-IDS, a decentralized, secure, flexible, scalable, and robust Blockchain and Federated Learning-based intrusion detection system. VFed-IDS is designed to identify cyber threats in the IoV while preserving privacy in connected vehicles. The proposed architecture consists of three main layers: the central layer, the local layer, and the Blockchain layer. The central layer includes the SDN Controller, responsible for training and aggregating the global model. The local layer comprises vehicles training individual models based on their private local datasets. The Blockchain layer introduces the Smart Contract VFed-SC, which manages the list of authenticated and collaborating vehicles in the Federated Learning process. It also hashes trained local model updates before transmitting them as transactions between the central and local layers. Simulation results demonstrate that VFed-IDS achieves a high accuracy rate of 99%, effectively enhancing the autonomous behavior of connected vehicles against cyber threats. © 2025 IEEE.},
note = {Journal Abbreviation: IEEE Int Conf Commun},
keywords = {Block-chain, Blockchain, Central layers, Computer crime, Connected vehicle, Connected Vehicles, Cyber threats, Cyberthreat detection, Cyberthreats, Cyberthreats Detection, Data privacy, Federated learning, Intelligent transport, Intelligent vehicle highway systems, Internet of things, Intrusion Detection, Intrusion Detection Systems, Learning systems, Network security, SDN, Threat detection, Traffic control, Vehicles},
pubstate = {published},
tppubtype = {inproceedings}
}
Over the past few years, there have been made significant strides in advancing the Internet of Vehicles (IoV), recognizing its strategic importance in Intelligent Transport Systems. The proliferation of connected and autonomous vehicles on the roads has propelled the IoV into the spotlight. However, addressing the specific demands of vehicular networks, such as low latency, high mobility, extensive connectivity of 5G/6G networks, and robust security, remains a substantial challenge. Therefore, there is a critical need for substantial progress in implementing a resilient Intrusion Detection System within the IoV ecosystem. This paper introduces VFed-IDS, a decentralized, secure, flexible, scalable, and robust Blockchain and Federated Learning-based intrusion detection system. VFed-IDS is designed to identify cyber threats in the IoV while preserving privacy in connected vehicles. The proposed architecture consists of three main layers: the central layer, the local layer, and the Blockchain layer. The central layer includes the SDN Controller, responsible for training and aggregating the global model. The local layer comprises vehicles training individual models based on their private local datasets. The Blockchain layer introduces the Smart Contract VFed-SC, which manages the list of authenticated and collaborating vehicles in the Federated Learning process. It also hashes trained local model updates before transmitting them as transactions between the central and local layers. Simulation results demonstrate that VFed-IDS achieves a high accuracy rate of 99%, effectively enhancing the autonomous behavior of connected vehicles against cyber threats. © 2025 IEEE.
6.
Valem, L. P.; Pedronette, D. C. G.; Allili, M. S.
Contrastive Loss Based on Contextual Similarity for Image Classification Proceedings Article
In: G., Bebis; V., Patel; J., Gu; J., Panetta; Y., Gingold; K., Johnsen; M.S., Arefin; S., Dutta; A., Biswas (Ed.): Lect. Notes Comput. Sci., pp. 58–69, Springer Science and Business Media Deutschland GmbH, 2025, ISBN: 03029743 (ISSN); 978-303177391-4 (ISBN), (Journal Abbreviation: Lect. Notes Comput. Sci.).
Abstract | Links | BibTeX | Tags: Adversarial machine learning, Classification accuracy, Contrastive Learning, Cross entropy, Experimental evaluation, Federated learning, Image classification, Image comparison, Image embedding, Images classification, Model generalization, Model robustness, Neighborhood information, Self-supervised learning, Similarity measure
@inproceedings{valemContrastiveLossBased2025a,
title = {Contrastive Loss Based on Contextual Similarity for Image Classification},
author = {L. P. Valem and D. C. G. Pedronette and M. S. Allili},
editor = {Bebis G. and Patel V. and Gu J. and Panetta J. and Gingold Y. and Johnsen K. and Arefin M.S. and Dutta S. and Biswas A.},
url = {https://www.scopus.com/pages/publications/85218461565?origin=resultslist},
doi = {10.1007/978-3-031-77392-1_5},
isbn = {03029743 (ISSN); 978-303177391-4 (ISBN)},
year = {2025},
date = {2025-01-01},
booktitle = {Lect. Notes Comput. Sci.},
volume = {15046 LNCS},
pages = {58–69},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {Contrastive learning has been extensively exploited in self-supervised and supervised learning due to its effectiveness in learning representations that distinguish between similar and dissimilar images. It offers a robust alternative to cross-entropy by yielding more semantically meaningful image embeddings. However, most contrastive losses rely on pairwise measures to assess the similarity between elements, ignoring more general neighborhood information that can be leveraged to enhance model robustness and generalization. In this paper, we propose the Contextual Contrastive Loss (CCL) to replace pairwise image comparison by introducing a new contextual similarity measure using neighboring elements. The CCL yields a more semantically meaningful image embedding ensuring better separability of classes in the latent space. Experimental evaluation on three datasets (Food101, MiniImageNet, and CIFAR-100) has shown that CCL yields superior results by achieving up to 10.76% relative gains in classification accuracy, particularly for fewer training epochs and limited training data. This demonstrates the potential of our approach, especially in resource-constrained scenarios. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.},
note = {Journal Abbreviation: Lect. Notes Comput. Sci.},
keywords = {Adversarial machine learning, Classification accuracy, Contrastive Learning, Cross entropy, Experimental evaluation, Federated learning, Image classification, Image comparison, Image embedding, Images classification, Model generalization, Model robustness, Neighborhood information, Self-supervised learning, Similarity measure},
pubstate = {published},
tppubtype = {inproceedings}
}
Contrastive learning has been extensively exploited in self-supervised and supervised learning due to its effectiveness in learning representations that distinguish between similar and dissimilar images. It offers a robust alternative to cross-entropy by yielding more semantically meaningful image embeddings. However, most contrastive losses rely on pairwise measures to assess the similarity between elements, ignoring more general neighborhood information that can be leveraged to enhance model robustness and generalization. In this paper, we propose the Contextual Contrastive Loss (CCL) to replace pairwise image comparison by introducing a new contextual similarity measure using neighboring elements. The CCL yields a more semantically meaningful image embedding ensuring better separability of classes in the latent space. Experimental evaluation on three datasets (Food101, MiniImageNet, and CIFAR-100) has shown that CCL yields superior results by achieving up to 10.76% relative gains in classification accuracy, particularly for fewer training epochs and limited training data. This demonstrates the potential of our approach, especially in resource-constrained scenarios. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
7.
Houda, Z. Abou El; Moudoud, H.; Brik, B.
When Federated Learning Meets Knowledge Distillation to Secure Consumer Edge Network Journal Article
In: IEEE Transactions on Consumer Electronics, vol. 71, no. 2, pp. 7192–7200, 2025, ISSN: 00983063 (ISSN).
Abstract | Links | BibTeX | Tags: Adversarial machine learning, Collaborative modeling, Communication efficiency, Differential privacy, Distributed environments, Edge consumer network, EDGE Networks, Federated learning, Knowledge distillation, Model training, Secure multi-party computation, Security challenges, TinyML
@article{abouelhoudaWhenFederatedLearning2025,
title = {When Federated Learning Meets Knowledge Distillation to Secure Consumer Edge Network},
author = {Z. Abou El Houda and H. Moudoud and B. Brik},
url = {https://www.scopus.com/pages/publications/105002607319?origin=resultslist},
doi = {10.1109/TCE.2025.3559004},
issn = {00983063 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {IEEE Transactions on Consumer Electronics},
volume = {71},
number = {2},
pages = {7192–7200},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Consumer networks face several security challenges due to the distributed nature of edge devices and the sensitive data they handle. Federated Learning (FL) presents a promising paradigm for collaborative model training in distributed environments. However, its implementation in edge consumer networks raises concerns about model heterogeneity, communication efficiency, and reverse engineering attacks. To address these issues, in this paper, we introduce SKDFL, a novel framework that leverages Knowledge Distillation (KD) and Secure Multi-Party Computation (SMPC) techniques to enhance communication efficiency while preserving data privacy in edge consumer networks. Through the use of KD, the distilled knowledge is transmitted between devices, significantly reducing communication overhead. Additionally, we incorporate lightweight encryption mechanisms to protect soft-labels from reverse engineering attacks using SMPC. We evaluate our proposed framework using two public datasets and demonstrate its efficiency in reducing communication costs, achieving up to a 92.4% reduction compared to conventional FL methods. Moreover, SKDFL achieves high performances in terms of accuracy and F1-score in both binary and multi-class classification while preserving the privacy of clients. Our obtained results show the potential of SKDFL to address the challenges of communication efficiency and data privacy in FL for edge consumer networks, paving the way for secure and efficient collaborative learning in consumer networks. © 1975-2011 IEEE.},
keywords = {Adversarial machine learning, Collaborative modeling, Communication efficiency, Differential privacy, Distributed environments, Edge consumer network, EDGE Networks, Federated learning, Knowledge distillation, Model training, Secure multi-party computation, Security challenges, TinyML},
pubstate = {published},
tppubtype = {article}
}
Consumer networks face several security challenges due to the distributed nature of edge devices and the sensitive data they handle. Federated Learning (FL) presents a promising paradigm for collaborative model training in distributed environments. However, its implementation in edge consumer networks raises concerns about model heterogeneity, communication efficiency, and reverse engineering attacks. To address these issues, in this paper, we introduce SKDFL, a novel framework that leverages Knowledge Distillation (KD) and Secure Multi-Party Computation (SMPC) techniques to enhance communication efficiency while preserving data privacy in edge consumer networks. Through the use of KD, the distilled knowledge is transmitted between devices, significantly reducing communication overhead. Additionally, we incorporate lightweight encryption mechanisms to protect soft-labels from reverse engineering attacks using SMPC. We evaluate our proposed framework using two public datasets and demonstrate its efficiency in reducing communication costs, achieving up to a 92.4% reduction compared to conventional FL methods. Moreover, SKDFL achieves high performances in terms of accuracy and F1-score in both binary and multi-class classification while preserving the privacy of clients. Our obtained results show the potential of SKDFL to address the challenges of communication efficiency and data privacy in FL for edge consumer networks, paving the way for secure and efficient collaborative learning in consumer networks. © 1975-2011 IEEE.
8.
Mehrban, A.; Houda, Z. A. El; Moudoud, H.; Brik, B.; Khoukhi, L.
A Blockchain-Enabled Multi-Layered Zero-Trust Security Framework for O-RAN Proceedings Article
In: Int. Wirel. Commun. Mob. Comput. Conf., IWCMC, pp. 1564–1569, Institute of Electrical and Electronics Engineers Inc., 2025, ISBN: 979-833150887-6 (ISBN), (Journal Abbreviation: Int. Wirel. Commun. Mob. Comput. Conf., IWCMC).
Abstract | Links | BibTeX | Tags: Access control, Block-chain, Blockchain, Data privacy, Federated learning, federated learning (FL), Internet of thing, Internet of things, Internet of Things (IoT), Interoperability, Learning systems, Mobile telecommunication systems, Multi-layered, Network architecture, Network security, Open access, Open radio access network, open radio access network (O-RAN), Radio, Radio access networks, Radio access technologies, Radio communication, Secure communication, Security, Security frameworks, Security risks, Transfer learning, Trusted computing
@inproceedings{mehrbanBlockchainEnabledMultiLayeredZeroTrust2025,
title = {A Blockchain-Enabled Multi-Layered Zero-Trust Security Framework for O-RAN},
author = {A. Mehrban and Z. A. El Houda and H. Moudoud and B. Brik and L. Khoukhi},
url = {https://www.scopus.com/pages/publications/105011345211?origin=resultslist},
doi = {10.1109/IWCMC65282.2025.11059720},
isbn = {979-833150887-6 (ISBN)},
year = {2025},
date = {2025-01-01},
booktitle = {Int. Wirel. Commun. Mob. Comput. Conf., IWCMC},
pages = {1564–1569},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {O-RAN (Open Radio Access Network) is a set of open and interoperable radio access technologies, guided by the O-RAN Alliance, that, despite an open ecosystem, introduces significant security risks, expanding the threat surface in 6G networks. Traditional perimeter-based security approaches are inadequate for O-RAN's highly distributed, multi-vendor environments, where Zero Trust Architecture (ZTA) becomes essential for robust security. To address these challenges, we propose a novel blockchain-based, decentralized Zero-Trust Framework specifically designed for O-RAN security. Our proposed framework comprises two key layers: the first layer utilizes Federated Learning (FL) and Transfer Learning (TL) for advanced attack detection, enabling distributed, privacy-preserving threat analysis across O-RAN nodes. The second layer enforces Zero Trust access control through a blockchain-based identity management system, ensuring tamper-resistant, real-time policy updates. This multi-layered framework provides adaptive threat detection and resilient access control, validated through simulations demonstrating high detection accuracy and robust access management with minimal impact on network performance, offering a scalable security solution for next-generation O-RAN deployments. © 2025 IEEE.},
note = {Journal Abbreviation: Int. Wirel. Commun. Mob. Comput. Conf., IWCMC},
keywords = {Access control, Block-chain, Blockchain, Data privacy, Federated learning, federated learning (FL), Internet of thing, Internet of things, Internet of Things (IoT), Interoperability, Learning systems, Mobile telecommunication systems, Multi-layered, Network architecture, Network security, Open access, Open radio access network, open radio access network (O-RAN), Radio, Radio access networks, Radio access technologies, Radio communication, Secure communication, Security, Security frameworks, Security risks, Transfer learning, Trusted computing},
pubstate = {published},
tppubtype = {inproceedings}
}
O-RAN (Open Radio Access Network) is a set of open and interoperable radio access technologies, guided by the O-RAN Alliance, that, despite an open ecosystem, introduces significant security risks, expanding the threat surface in 6G networks. Traditional perimeter-based security approaches are inadequate for O-RAN's highly distributed, multi-vendor environments, where Zero Trust Architecture (ZTA) becomes essential for robust security. To address these challenges, we propose a novel blockchain-based, decentralized Zero-Trust Framework specifically designed for O-RAN security. Our proposed framework comprises two key layers: the first layer utilizes Federated Learning (FL) and Transfer Learning (TL) for advanced attack detection, enabling distributed, privacy-preserving threat analysis across O-RAN nodes. The second layer enforces Zero Trust access control through a blockchain-based identity management system, ensuring tamper-resistant, real-time policy updates. This multi-layered framework provides adaptive threat detection and resilient access control, validated through simulations demonstrating high detection accuracy and robust access management with minimal impact on network performance, offering a scalable security solution for next-generation O-RAN deployments. © 2025 IEEE.
9.
Moudoud, H.; Houda, Z. A. El; Brik, B.; Jan, M. A.; Alshawi, B.
Advancing Robustness and Privacy in Federated Learning for Secure Autonomous Vehicle Systems Journal Article
In: IEEE Transactions on Consumer Electronics, vol. 71, no. 2, pp. 6183–6192, 2025, ISSN: 00983063 (ISSN).
Abstract | Links | BibTeX | Tags: Augmented intelligence of things, Autonomous driving, Autonomous vehicle system, Autonomous Vehicles, Critical challenges, Differential privacy, Distributed machine learning, Driving environment, Federated learning, Privacy, Security, Security and privacy, Vehicle system
@article{moudoudAdvancingRobustnessPrivacy2025,
title = {Advancing Robustness and Privacy in Federated Learning for Secure Autonomous Vehicle Systems},
author = {H. Moudoud and Z. A. El Houda and B. Brik and M. A. Jan and B. Alshawi},
url = {https://www.scopus.com/pages/publications/105002620945?origin=resultslist},
doi = {10.1109/TCE.2025.3558999},
issn = {00983063 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {IEEE Transactions on Consumer Electronics},
volume = {71},
number = {2},
pages = {6183–6192},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {The rapid development of Autonomous Vehicle Systems (AVS) is transforming transportation, enabling safer, more efficient mobility. However, ensuring the security and privacy of sensitive data generated by AVS remains a major challenge. Federated Learning (FL) has emerged as a promising solution for AVS by enabling distributed machine learning across connected vehicles without sharing raw data, thereby enhancing privacy. Despite these advantages, FL faces critical challenges in autonomous driving environments, including high communication overhead, latency, and vulnerability to adversarial attacks. To address these challenges, we propose SecureFL, a novel framework designed to enhance the robustness and privacy of FL in autonomous vehicle systems. First, we propose a Federated Gradient Sign Attack (FGSA) detection mechanism using an ensemble of classifiers to identify and mitigate adversarial attacks that attempt to corrupt the global learning model. Then, we integrate a Graph Neural Network (GNN)-based reputation system that evaluates the reliability of vehicles based on data quality, prioritizing contributions from trustworthy sources, and dynamically adjusting participation in the FL process. Finally, we introduce an uplink scheduling mechanism utilizing a rate-splitting multiple access (RSMA) technique to optimize data transmission and reduce latency, ensuring efficient communication across the AVS network. The framework’s effectiveness is validated through simulations in real-world AVS environments, demonstrating SecureFL’s capability to strengthen security, privacy, and communication efficiency in federated learning for autonomous vehicles. This work contributes to advancing the robustness and privacy of FL, enabling safer and more secure autonomous driving. © 1975-2011 IEEE.},
keywords = {Augmented intelligence of things, Autonomous driving, Autonomous vehicle system, Autonomous Vehicles, Critical challenges, Differential privacy, Distributed machine learning, Driving environment, Federated learning, Privacy, Security, Security and privacy, Vehicle system},
pubstate = {published},
tppubtype = {article}
}
The rapid development of Autonomous Vehicle Systems (AVS) is transforming transportation, enabling safer, more efficient mobility. However, ensuring the security and privacy of sensitive data generated by AVS remains a major challenge. Federated Learning (FL) has emerged as a promising solution for AVS by enabling distributed machine learning across connected vehicles without sharing raw data, thereby enhancing privacy. Despite these advantages, FL faces critical challenges in autonomous driving environments, including high communication overhead, latency, and vulnerability to adversarial attacks. To address these challenges, we propose SecureFL, a novel framework designed to enhance the robustness and privacy of FL in autonomous vehicle systems. First, we propose a Federated Gradient Sign Attack (FGSA) detection mechanism using an ensemble of classifiers to identify and mitigate adversarial attacks that attempt to corrupt the global learning model. Then, we integrate a Graph Neural Network (GNN)-based reputation system that evaluates the reliability of vehicles based on data quality, prioritizing contributions from trustworthy sources, and dynamically adjusting participation in the FL process. Finally, we introduce an uplink scheduling mechanism utilizing a rate-splitting multiple access (RSMA) technique to optimize data transmission and reduce latency, ensuring efficient communication across the AVS network. The framework’s effectiveness is validated through simulations in real-world AVS environments, demonstrating SecureFL’s capability to strengthen security, privacy, and communication efficiency in federated learning for autonomous vehicles. This work contributes to advancing the robustness and privacy of FL, enabling safer and more secure autonomous driving. © 1975-2011 IEEE.
10.
Valem, L. P.; Pedronette, D. C. G.; Allili, M. S.
Contrastive Loss Based on Contextual Similarity for Image Classification Proceedings Article
In: G., Bebis; V., Patel; J., Gu; J., Panetta; Y., Gingold; K., Johnsen; M.S., Arefin; S., Dutta; A., Biswas (Ed.): Lect. Notes Comput. Sci., pp. 58–69, Springer Science and Business Media Deutschland GmbH, 2025, ISBN: 03029743 (ISSN); 978-303177391-4 (ISBN), (Journal Abbreviation: Lect. Notes Comput. Sci.).
Abstract | Links | BibTeX | Tags: Adversarial machine learning, Classification accuracy, Contrastive Learning, Cross entropy, Experimental evaluation, Federated learning, Image classification, Image comparison, Image embedding, Images classification, Model generalization, Model robustness, Neighborhood information, Self-supervised learning, Similarity measure
@inproceedings{valemContrastiveLossBased2025,
title = {Contrastive Loss Based on Contextual Similarity for Image Classification},
author = {L. P. Valem and D. C. G. Pedronette and M. S. Allili},
editor = {Bebis G. and Patel V. and Gu J. and Panetta J. and Gingold Y. and Johnsen K. and Arefin M.S. and Dutta S. and Biswas A.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85218461565&doi=10.1007%2f978-3-031-77392-1_5&partnerID=40&md5=cf885303646c3b1a4f4eacb87d02a2b6},
doi = {10.1007/978-3-031-77392-1_5},
isbn = {03029743 (ISSN); 978-303177391-4 (ISBN)},
year = {2025},
date = {2025-01-01},
booktitle = {Lect. Notes Comput. Sci.},
volume = {15046 LNCS},
pages = {58–69},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {Contrastive learning has been extensively exploited in self-supervised and supervised learning due to its effectiveness in learning representations that distinguish between similar and dissimilar images. It offers a robust alternative to cross-entropy by yielding more semantically meaningful image embeddings. However, most contrastive losses rely on pairwise measures to assess the similarity between elements, ignoring more general neighborhood information that can be leveraged to enhance model robustness and generalization. In this paper, we propose the Contextual Contrastive Loss (CCL) to replace pairwise image comparison by introducing a new contextual similarity measure using neighboring elements. The CCL yields a more semantically meaningful image embedding ensuring better separability of classes in the latent space. Experimental evaluation on three datasets (Food101, MiniImageNet, and CIFAR-100) has shown that CCL yields superior results by achieving up to 10.76% relative gains in classification accuracy, particularly for fewer training epochs and limited training data. This demonstrates the potential of our approach, especially in resource-constrained scenarios. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.},
note = {Journal Abbreviation: Lect. Notes Comput. Sci.},
keywords = {Adversarial machine learning, Classification accuracy, Contrastive Learning, Cross entropy, Experimental evaluation, Federated learning, Image classification, Image comparison, Image embedding, Images classification, Model generalization, Model robustness, Neighborhood information, Self-supervised learning, Similarity measure},
pubstate = {published},
tppubtype = {inproceedings}
}
Contrastive learning has been extensively exploited in self-supervised and supervised learning due to its effectiveness in learning representations that distinguish between similar and dissimilar images. It offers a robust alternative to cross-entropy by yielding more semantically meaningful image embeddings. However, most contrastive losses rely on pairwise measures to assess the similarity between elements, ignoring more general neighborhood information that can be leveraged to enhance model robustness and generalization. In this paper, we propose the Contextual Contrastive Loss (CCL) to replace pairwise image comparison by introducing a new contextual similarity measure using neighboring elements. The CCL yields a more semantically meaningful image embedding ensuring better separability of classes in the latent space. Experimental evaluation on three datasets (Food101, MiniImageNet, and CIFAR-100) has shown that CCL yields superior results by achieving up to 10.76% relative gains in classification accuracy, particularly for fewer training epochs and limited training data. This demonstrates the potential of our approach, especially in resource-constrained scenarios. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.