Artificial intelligence (AI) has revolutionized medical imaging with automated disease detection, image segmentation, diagnosis, prognosis prediction, and clinical decision support across various imaging modalities, such as X-ray, computed tomography (CT), magnetic resonance imaging (MRI), ultrasound, positron emission...
S Sur, Mandal Rakesh Kumar, Chanda Debanil· Zenodo (CERN European Organi...· 0 citations
Artificial intelligence (AI) has revolutionized medical imaging with automated disease detection, image segmentation, diagnosis, prognosis prediction, and clinical decision support across various imaging modalities, such as X-ray, computed tomography (CT), magnetic resonance imaging (MRI), ultrasound, positron emission...
S Sur, Mandal Rakesh Kumar, Chanda Debanil· Zenodo (CERN European Organi...· 0 citations
This survey develops a unified taxonomy that systematically integrates learning paradigms, agent architectures, coordination mechanisms, deployment models, application domains, and evaluation frameworks from a common analytical perspective and provides a structured foundation for future advances in intelligent agent sy...
Elias Dritsas, M. Trigka· Evolutionary Intelligence· 0 citations
AF-EdgeRL is proposed, a novel Byzantine-resilient Asynchronous Federated Reinforcement Learning framework tailored for distributed resource allocation and dynamic task offloading and establishes theoretical convergence guarantees under non-convex reinforcement learning objectives.
Daniel Merrow, Tember L. Nair, Lucas Farnandez· International Journal of App...· 0 citations
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Executive Overview This consolidated release of Paper X unifies empirical findings, mathematical foundations, and real-world implementation proofs for AuraOS—a local-first, zero-extraction computational architecture designed to eliminate recurring cloud SaaS overhead and API token extraction. By decoupling spatial reco...
Dallas Courchene· Zenodo (CERN European Organi...· 0 citations
Clinical modeling experience is established as a promising collaborative object beyond conventional parameter-centric FL and point toward federated autonomous clinical intelligence.
Reproducibility capsule for FusionNet Lite, a lightweight sequence-fusion deep-learning pipeline for predictive maintenance in industrial IoT systems. The campaign evaluates FusionNet Lite alongside CNN, BiLSTM, MLP, and CNN-LSTM baselines using centralized and FedAvg federated training across five seeds. The capsule i...
Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources without directly exchanging data. However, traditional FL methods often rely on a central server, raising significant privacy concerns, especially when dealing with sensitive data residing on edge devic...
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Acute lateral ankle sprain is among the most frequent injuries in sports medicine, and its high recurrence rate and propensity toward chronic ankle instability (CAI) constitute a persistent clinical challenge. Conventional risk assessment—relying on subjective questionnaires, physical examination, and clinical experien...
Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources without directly exchanging data. However, traditional FL methods often rely on a central server, raising significant privacy concerns, especially when dealing with sensitive data residing on edge devic...
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources without direct data sharing. However, traditional FL schemes are vulnerable to privacy attacks, particularly model inversion attacks, which can reveal sensitive information about the underlying data. Th...
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Residential load forecasting underpins demand response and local energy trading, yet privacy regulations such as the EU GDPR prevent service providers from collecting fine-grained household consumption data. Federated learning (FL) enables collaborative training without raw data exchange, and client clustering widely m...
Ran Zheng, Sara Barja-Martínez, Mònica Aragüés‐Peñalba et al.· Energy Reports· 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.
Microsoft Research Blog· microsoft.comOct 12, 2026
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.
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.
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