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Grégoire Déletang

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Federated Learning in Healthcare: A Bibliometric Analysis of Privacy, Security, and Adversarial Threats (2021-2024)

Federated Learning in Healthcare: A Bibliometric Analysis of Privacy, Security, and Adversarial Threats (2021-2024) — Federated Learning (FL) has rapidly emerged as a transformative machine learning approach, enabling healthcare institutions to collaboratively build predictive models without compromising patient data privacy. As healthcare increasingly adopts digital technologies, federated learning offers promising solutions to critical issues such as data privacy, security, data poisoning, and adversarial attacks. Despite the recognized potential of FL, significant gaps persist in existing research, particula — Citations: 21.

SLIM: Stateless-Based Lightweight Sharding Mechanism for Secure Data Interchange in Blockchain-Enabled Internet of Vehicles

SLIM: Stateless-Based Lightweight Sharding Mechanism for Secure Data Interchange in Blockchain-Enabled Internet of Vehicles — The emergence of Blockchain-enabled Internet of Vehicles stands as a critical method for secure data exchange between vehicles and traffic infrastructures. Yet, significant challenges persist, particularly in terms of limited fault tolerance and constraints associated with storage and computational resources. In this study, we introduce the Stateless-based Lightweight Sharding Mechanism (SLIM) for enhanced secure data exchange. This mechanism is built on three key strategies: 1) SLIM incorporate — Citations: 0.