Abstract 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 Hannouss...
Kahkashan Kouser, Mohammad 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
Reach audiences
Advertise in front of researchers, engineers, and readers.
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....
Clustered Federated Learning (FL) partitions a client population into groups of similar local distributions and trains one specialized model per cluster, mitigating client drift that degrades single-model methods under non-IID data. Prior methods discover cluster structure inside the training loop through gradient simi...
World models learn state evolution from trajectories, making access to temporal context a central training requirement. Federated learning can use distributed records, while ownership boundaries within a trajectory restrict the examples each client can construct. Our study benchmarks this cross-time setting through hou...
Yi-Pan Wei, Zhao-Kun Yan, Zi-Ming Hong et al.· 0 citations
We consider a distributed learning task with agents that have correlated data. Specifically, the label of an agent depends on the input of other agents for the same sample, and these inputs are also correlated. Correlated data is the reality when agents share the same environment. Existing decentralized methods, such a...
Modern state space models (SSMs), such as Mamba2, provide a compelling alternative to transformers by combining linear-time sequence modeling with recurrent state-space dynamics. However, the behavior of SSMs in distributed learning settings remains poorly understood. In particular, the existing standard federated lear...
Adam Piaseczny, Md Kamran Chowdhury Shisher, Shi-Qiang Wang et al.· 0 citations
Theranostics links disease characterization with treatment selection and response assessment, creating a framework in which diagnostic information directly informs individualized therapy. Recent advances in artificial intelligence (AI), machine learning (ML), deep learning, radiomics, radiogenomics, and multimodal data...
Shivika Singh*· Zenodo (CERN European Organi...· 0 citations
Engineering colleges across India face a critical challenge, with approximately 28% of enrolled students failing to complete their degrees on time. In regions like Vidarbha, Maharashtra, this rate is exacerbated by localized financial stress, first-generation academic backgrounds, and constrained support systems. Tradi...
Pranita G. Bais¹*, Syed Asif Syed Gaffar1, Arpita Mukund Jondhale1, Gayatri Shriram Bharose1, Vinod K. Pawar2, Anand K. Pathrikar3· Zenodo (CERN European Organi...· 0 citations
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.
StudentSim is a research system for evaluating and improving AI tutoring tools before they are tested with learners. It models how individuals may engage with tutors, giving feedback to improve tutor performance across subjects like chess, writing, and mathematics. The post Simulating student learning for better AI tutoring appeared first on Microsoft Research.
MIT News · Artificial Intelligence· news.mit.eduOct 7, 2026
Students in MIT’s Concourse program delve deeply into the human condition, debate challenging questions, and learn to develop judgment about issues that can’t be quantified.
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
