The proliferation of multi-cloud architectures has intensified challenges in phishing detection, as security intelligence becomes fragmented across heterogeneous cloud infrastructures governed by stringent privacy regulations and data sovereignty constraints. Conventional centralized threat detection paradigms necessit...
Shakir Khan, Arvind Panwar, Achin Jain et al.· Scientific Reports· 0 citations
Re-created code for the PLoS One article e0322749 (2025): nuclei segmentation (SegNet) and classification (DenseNet121) on PanNuke with Hyperband tuning, TensorFlow Lite quantization and federated learning. Includes the result tables underlying the reproduced results.
Anjir Ahmed Chowdhury, S M Hasan Mahmud, Md Palash Uddin et al.· Zenodo (CERN European Organi...· 0 citations
Résumé (FR) Ce document, produit avec l'assistance de Gemini 3 Raisonnement, est publié sous licence Apache 2.0. Il constitue une publication défensive volontaire (antériorité) entrant dans l'état de la technique selon les législations applicables (EPC Art. 54(2); French IPC Art. L 611-11; cf. 35 U.S.C. §102(a)). Face...
Xavier Pillet· Zenodo (CERN European Organi...· 0 citations
The integration of machine learning (ML) into Internet of Things (IoT) systems presents transformative opportunities across various domains but also introduces numerous security challenges. This study explores the role of ML in enhancing IoT functionalities, particularly in data analytics, security, optimization, and u...
Kamel-Dine Haouam, Mourad Benmalek· International Journal of Inf...· 0 citations
Reach audiences
Advertise in front of researchers, engineers, and readers.
Privacy-preserving machine learning in Vehicular Ad Hoc Networks (VANETs) must address sensitive on-board data, limited ground-truth annotations, and verifiable model-update integrity. This paper proposes ZK-FL, a decentralized federated learning framework for Intelligent Transportation Systems (ITS) scene classificati...
M. Medvei, Miruna-Mihaela Modiga, Iulian Aciobăniței et al.· Frontiers in Future Transpor...· 0 citations
Accurate 3D delineation of bone tumors and metastatic lesions from volumetric CT is essential for diagnosis, staging, radiotherapy planning, and treatment monitoring, yet manual contouring is time consuming and variable across observers. Although modern 3D CNN/Transformer segmenters achieve strong centralized performan...
Yongan Liu, Tao Yu, Dan Su et al.· Scientific Reports· 0 citations
Machine-learning phishing URL detectors routinely report accuracies above 97%, yet four weaknesses undermine these results: absent statistical testing, unfair latency benchmarking, uninterpretable predictions, and an inability to use data held privately across organisations. We address all four on the Hannousse-Yahio...
Kahkashan Kouser, M. Aknan, Onkar Singh et al.· Scientific Reports· 0 citations
Résumé (FR) Ce document, produit avec l'assistance de Gemini 3 Raisonnement, est publié sous licence Apache 2.0. Il constitue une publication défensive volontaire (antériorité) et entre de ce fait dans l'état de la technique dès sa publication en vertu des législations sur les brevets applicables : art. 54(2) CBE (Conv...
Xavier Pillet· Zenodo (CERN European Organi...· 0 citations
Résumé (FR) Ce document, produit avec l'assistance de Gemini 3 Raisonnement, est publié sous licence Apache 2.0. Il constitue une publication défensive volontaire (antériorité) et entre de ce fait dans l'état de la technique dès sa publication en vertu des législations sur les brevets applicables : art. 54(2) CBE (Conv...
Xavier Pillet· Zenodo (CERN European Organi...· 0 citations
Smart city Internet of Things (IoT) networks is generating a continuous stream of heterogeneous sensor data that tends to require a timely analysis under the strict energy, latency, and computational constraints. Existing cloud-edge learning approaches have improved IoT intelligence, but they often treat feature learni...
Kathiresan Jayabalan, P. Sreelatha, T. Dakshinamurthy et al.· Discover Internet of Things· 0 citations
Federated Learning (FL) is a privacy-oriented learning paradigm that enables collaborative model training while keeping training data local to participating clients. However, it does not guarantee that clients submit policy-compliant contributions or that aggregators process admitted contributions correctly. Existing v...
Dominik Roy George, Varesh Mishra, Aysajan Abidin· 0 citations
Federated learning lets many clients train a shared model together without ever sending their private data to a central server. Each client shares only a model update, and this update should reveal far less about the client than its raw training examples would. This premise is what protects the privacy of the clients....
Assistant Professor Benjamin Lindquist studies how engineers deliberately injected randomness into early AI, giving rise to deep-learning models that sometimes hallucinate.
Learn how Google DeepMind and GovTech Singapore uses lightweight on-device Gemma models to intercept conversational scams in private chats with zero cloud data egress.
MIT News · Artificial Intelligence· news.mit.eduOct 13, 2026
Machine-learning networks can learn without detailed instructions from a designer. Those that do may offer insight into how the brain reorganizes itself during learning.
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
