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federated learning

2,057 papers

#machine learning Open access Sep 2026

Task-Centric Personalized Federated Fine-Tuning of Language Models

Federated Learning (FL) has emerged as a promising technique for training language models on distributed and private datasets of diverse tasks. However, aggregating models trained on heterogeneous tasks often degrades the overall performance of individual clients. To address this issue, Personalized FL (pFL) aims to cr...

Gabriel U. Talasso, Meghdad Kurmanji, Allan M. de Souza et al. · 0 citations
#graph neural networks Dataset Open access Sep 2026

HUNTINGTON'S DISEASE ENRICHED COHORT

This Zenodo record contains a free, evaluation-grade sample of 10,000 synthetic patient records — a genuine, unmodified subset of the full production dataset, not a separate re-generation. It is provided so that researchers, ML engineers, and clinical data scientists can inspect the schema, phenotypic depth, and biomar...

CODE SENTINEL DATA, Chekerskyi Maksym · 0 citations
#reinforcement learning Open access Sep 2026

Spatio-Temporal Graph Transformer MARL for Resilient Point of Common Coupling Control in Networked Grid-Tied Micro-grids

The increasing penetration of renewable energy sources introduces significant variability and uncertainty into grid-connected microgrids (MGs), affecting voltage stability, frequency regulation, power quality, fault ride-through capability, and operational resilience at the Point of Common Coupling (PCC). These challen...

Nicholas Nyaika, Cosmas Mwikirize, Andrew Katumba (PhD)3 · 0 citations
#artificial intelligence Review Open access Sep 2026

The Evolving Security of the Internet of Vehicles: A Survey from Classical Machine Learning to Large Language Models, Adversarial Robustness, and Explainable AI

The Internet of Vehicles (IoV) connects vehicles, roadside infrastructure, and cloud or edge platforms to support safer and more efficient transportation. This connectivity also exposes vehicular networks to message spoofing, denial-of-service, and man-in-the-middle attacks, and to threats that target the machine-learn...

Meisam Sharifi Sani, Saeid Iranmanesh, Raad Raad · 0 citations
#federated learning Open access Sep 2026

Divide-and-Conquer Decomposition Strategies for Real-Time Artificial Intelligence Decision Systems: A Comparative Survey

Abstract—Real-time artificial intelligence (AI) decision systems must resolve complex inputs — a video frame, a network flow, a multi-agent traffic scene — within a bounded time budget. A recurring, if rarely named, strategy for meeting this budget is the classical divide-and-conquer (D&C) paradigm: partition the probl...

Falak Ali · 0 citations
#federated learning Open access Sep 2026

Real-Time Decision Processing in Artificial Intelligence Systems: A Survey of Scheduling, Inference, and Security Foundations

Abstract—Real-time artificial intelligence (AI) decision systems must satisfy three constraints that conventional machine learning pipelines rarely address together: bounded inference latency, deterministic execution under a real-time operating system (RTOS), and resilience against adversarial or unauthorized interfere...

Falak Ali · 0 citations
#federated learning Open access Sep 2026

Real-Time Decision Processing in Artificial Intelligence Systems: A Survey of Scheduling, Inference, and Security Foundations

Abstract—Real-time artificial intelligence (AI) decision systems must satisfy three constraints that conventional machine learning pipelines rarely address together: bounded inference latency, deterministic execution under a real-time operating system (RTOS), and resilience against adversarial or unauthorized interfere...

Falak Ali · 0 citations
#federated learning Open access Sep 2026

Divide-and-Conquer Decomposition Strategies for Real-Time Artificial Intelligence Decision Systems: A Comparative Survey

Abstract—Real-time artificial intelligence (AI) decision systems must resolve complex inputs — a video frame, a network flow, a multi-agent traffic scene — within a bounded time budget. A recurring, if rarely named, strategy for meeting this budget is the classical divide-and-conquer (D&C) paradigm: partition the probl...

Falak Ali · 0 citations
#federated learning Review Open access Sep 2026

Towards federated learning for face recognition: a comprehensive review of secure and privacy-preserving techniques and prospective research

A systematic, PRISMA-guided review of 50 publications released between 2020 and 2026, identified through searches of IEEE Xplore, ACM Digital Library, SpringerLink, arXiv, Google Scholar, and DBLP, on face recognition frameworks that use federated learning.

Ajnas Muhammed, J. Marcos, Nuno Gonçalves · 0 citations
#federated learning Review Open access Sep 2026

Intelligent Real-Time Healthcare and Biomedical Monitoring Systems: A Narrative Review of AI, IoT, and Emerging Technologies

Background: Chronic disease management, an ageing global population, and the aftermath of the COVID-19 pandemic have pushed real-time, continuous health monitoring from a research curiosity toward routine clinical practice. Artificial intelligence (AI), the Internet of Medical Things (IoMT), edge computing, and next-ge...

Abdussalam Elhanashi, Sergio Saponara · 0 citations

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MIT News · Artificial Intelligence Oct 23, 2026

Q&A: How AI came to model the “messy” human mind

Assistant Professor Benjamin Lindquist studies how engineers deliberately injected randomness into early AI, giving rise to deep-learning models that sometimes hallucinate.

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