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1,863 papers
This dataset accompanies the manuscript “Fair-VARO: Balancing Reliability and Participation Fairness in Vehicular Federated Learning.” The study investigates the trade-off between update completion reliability and successful-participation fairness in vehicular federated learning using SUMO-based traffic simulations and...
Abstract In this paper, we present a novel one-shot federated clustering approach based on evolving fuzzy clustering and shuffler-based anonymization. In federated clustering, each round reveals additional model information to the server and can increase the opportunity to infer properties of local datasets, even when...
INTRODUCTION: The rapid integration of distributed renewable energy and ICT infrastructure has made interconnected multi-microgrid distribution systems essential for low-carbon power grids, yet centralized coordination exposes sensitive operational data to privacy risks, including eavesdropping and membership inference...
Managing offloaded tasks efficiently is vital for keeping devices fast and responsive. The main hurdle is finding a way to schedule and run these incoming workloads without starving local applications of critical CPU, RAM, and energy. This study evaluates how Federated Learning (FL) and Reinforcement Learning (RL) can...
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This paper provides a structured methodological review of intrusion detection systems (IDS) in the field of cybersecurity, with particular attention to Explainable Graph-Structured Reinforcement Learning (XGRL). Instead of presenting experimental evaluations, the study concentrates on analyzing design principles and me...
The rapid expansion of solar energy has accelerated the adoption of Artificial Intelligence (AI) to improve forecasting accuracy, photovoltaic performance, intelligent control, fault diagnosis, energy storage management, and smart grid operation. Recent advances in machine learning, deep learning, Digital Twins, Physic...
Advanced public health services are leveraging Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) to revolutionize their functions by harnessing complex data for analysis, forecasting of disease outbreaks, and making intelligent decisions. They are covered in this review and used in epidemiolog...
The El-Rakhawi Comprehensive Framework for Precision Detection and Targeted Nanotherapy of Laryngeal Cancer (DOI: 10.5281/zenodo.23297264) presents a groundbreaking multidisciplinary framework across six volumes: (1) Precision Detection via AI acoustic biomarkers and salivary liquid biopsy; (2) Targeted Nanotherapy usi...
The session Federated learning offers a promising approach for enabling collaborative scientific discovery while preserving the privacy of sensitive data across institutions. This BOF brought together researchers and practitioners to discuss trustworthy, privacy-preserving federated learning frameworks tailored for sci...
Abstract Sixth generation (6G) networks require intelligent and privacy-preserving solutions to handle traffic complexity, resource allocation, and security challenges. While Large Language Models (LLMs) offer advanced reasoning and Federated Artificial Intelligence (FAI) enables distributed learning without directly s...
A new machine-learning approach broke records for weather forecasting for an important time frame in emergency planning.
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.
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.
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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