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
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...
Mirabela Melinda Medvei, Miruna-Mihaela Modiga, Iulian Aciobăniţei 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
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
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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....
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...
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.
Training AI agents with reinforcement learning can be challenging because their tools, context, and decision-making are managed by complex frameworks. Agent Lightning connects existing agents to RL training, making it easier to improve them without rebuilding them. The post Agent Lightning v1.0: A 3,500-Line Lightweight Agentic RL Framework for Training Agents with Real Harnesses appeared first on Microsoft Research.
MIT News · Artificial Intelligence· news.mit.eduOct 6, 2026
