Code, prespecified analysis protocol, leakage unit tests and run-level results accompanying the article 'Federated Semi-Supervised Graph Neural Networks for the Prediction of Gestational and Other Diabetes from Tabular Records: A Leakage-Controlled Evaluation'. No patient data are included; the datasets are available f...
Gonzalo Daniel· Zenodo (CERN European Organi...· 0 citations
Artificial intelligence (AI) is increasingly being applied across the energy sector to support forecasting, diagnosis, optimization, control, and system planning. This review synthesizes common applications of AI in electricity-centered energy systems, including load and renewable-energy forecasting, electricity-market...
Bibek Ghimire· Zenodo (CERN European Organi...· 0 citations
Code, prespecified analysis protocol, leakage unit tests and run-level results accompanying the article 'Federated Semi-Supervised Graph Neural Networks for the Prediction of Gestational and Other Diabetes from Tabular Records: A Leakage-Controlled Evaluation'. No patient data are included; the datasets are available f...
Gonzalo Daniel, Venkatesan M· Zenodo (CERN European Organi...· 0 citations
Autonomous AI-Based Cloud Security Monitoring and Attack Prediction System Using Deep Neural Networks presents a comprehensive approach to modern cloud cybersecurity by combining cloud monitoring, artificial intelligence, deep learning, anomaly detection, attack classification, threat prediction, risk assessment, and a...
Anantha Raman Rathinam, M. Sakthivel, Dr. J. Gladson Maria Britto· Zenodo (CERN European Organi...· 0 citations
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Federated learning reduces the need to centralize raw data, but does not prevent privacy leakage from client updates, aggregation messages, or the final released model. This work compares local differential privacy (LDP), central differential privacy (CDP), record-level differentially private stochastic gradient descen...
This repository contains the reproducibility materials for the FedCRM-DP study, a privacy-preserving cross-silo federated learning framework combining federated optimization, per-client record-level differential privacy, and SecAgg+ secure aggregation. The package contains source code, frozen experimental configuration...
Nikhil Donapati· Zenodo (CERN European Organi...· 0 citations
This study evaluates federated continual learning for heart failure risk prediction under changing clinical data conditions. Using a simulated 12-month, five-site dataset, it compares attention-based continual learning with FedAvg, FedProx, linear, and replay-based approaches. The findings show that apparent benefits o...
P. Senthilkumar· Zenodo (CERN European Organi...· 0 citations
Disease diagnosis using medical images has become an indispensable part of healthcare today. This necessitates the development of smart and effective models. This research work proposes a MediFusionNet, a hybrid web-based CNN diagnostic tool for multi-disease diagnosis based on feature fusion between VGG16 and MobileNe...
Rahul Anand T., Prithiviraj R., Mohamed Bisail M. et al.· Journal of Information Techn...· 0 citations
Generative AI depends on training with very large volumes of data, and the acquisition and use of that data has become a focal point for copyright infringement and for privacy and security risks. This paper examines how generative AI training data is governed and what security problems it raises. It compares the reason...
Hui-Yao Jian· International Journal of Res...· 0 citations
*** PREPRINT / AUTHOR-ACCEPTED VERSION ***This paper was presented at the conference and is the author-accepted camera-ready version. It is posted here for self-archiving purposes in accordance with the IEEE Author Posting Policy prior to official publication and indexing in IEEE Xplore. Abstract—Deepfake-as-a-Service...
Akanksha Raghvesh, Kiran Paul Kanikaram· Zenodo (CERN European Organi...· 0 citations
This paper addresses vulnerabilities in standard federated learning aggregation against gradient inversion attacks by evaluating three layered privacy-enhancing mechanisms: classical differential privacy (DP) noise injection, homomorphic encryption, and a novel quantum-inspired random unitary rotation of embedding vect...
The rapid evolution of sixth-generation (6G) wireless networks is increasing the demand for AI-enabled edge intel ligence that can operate across devices with different capabil ities, resource constraints, and availability. Existing distributed learning paradigms, including Federated Learning (FL) and Split Learning (S...
SrushtiSurpur, Panagiotis Marantis, Kostas Ramantas et al.· Zenodo (CERN European Organi...· 0 citations
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
