Decentralized learning networks based on blockchain are based on strongly incentivized models and token economics to promote collaboration. Properly structured incentive mechanisms can be used to ensure high rates of participation, data sharing, and sustained engagement in the long term, whereas token economics provide...
The TaTFedLA‐based malware detection achieves the accuracy, Mean Average Precision, False Positive Rate (FPR), loss, Mean Square Error (MSE), Root MSE, bandwidth, energy, precision, recall and F‐measure, of 94.99%.
R. Bhavani, Veeramalai Sankaradass, I. V et al.· International Journal of Net...· 0 citations
Learning analytics platforms increasingly use behavioral traces to model engagement, navigation, and self-directed learning dynamics in virtual learning environments (VLEs). However, most current approaches prioritize predictive accuracy, centralize sensitive student records, and rarely analyze the stability of explora...
Rommel Gutierrez, Aracely Mera-Navarrete, Ana Cristina Villegas et al.· Frontiers in Artificial Inte...· 0 citations
Reference implementation of SWIFT, a wait-free decentralized federated learning algorithm in which clients never block on one another: each broadcasts its model to its neighbours and averages in whatever has arrived. SWIFT is the first asynchronous decentralized federated learning algorithm to obtain the optimal O(1/sq...
Marco Bornstein, Tahseen Rabbani, Evan Wang et al.· Zenodo (CERN European Organi...· 0 citations
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Lithium-ion batteries (LIBs) are widely used in electric vehicles (EVs), renewable energy storage systems (ESSs), and smart grids, but accurate degradation and remaining useful life (RUL) prediction remains challenging under sparse data and distributed data-storage conditions. This study proposes a Federated Physics-In...
Ali M. Eltamaly, S. Al-Senaidi· Electronics· 0 citations
Healthcare information technology (IT) infrastructure has grown substantially in complexity alongside the accelerating adoption of electronic health records, clinical decision support systems, telehealth platforms, and patient-facing digital services. Managing this infrastructure reliably under high availability expect...
Nayan Kumar Sureshbhai Patel· Journal of Information Syste...· 0 citations
This research introduces a decentralized optimization framework for improved operational performance of 11kV, 32-bus Nsukka Radial Distribution Network (RDN), using a privacy-preserving decentralized optimization framework known as Federated Learning (FL).
Gerald I. Okwe, Ijeoma Uneze, Isidore U. Uju et al.· International journal of rec...· 0 citations
This dataset package is associated with the study “HAPV-FEL: Historical Consensus Verification and Recoverable Trust for Privacy-Preserving Federated Transaction Risk Detection.” It contains structured CSV representations corresponding to the IBM AML and SAML-D datasets used for cross-institutional transaction risk det...
Deep Learning (DL) techniques are widely deployed in critical applications such as autonomous driving, healthcare, and intelligent infrastructure. Core paradigms—Deep Neural Networks (DNNs), Deep Reinforcement Learning (DRL), Federated Learning (FL), and Transfer Learning (TL)—remain vulnerable to adversarial attacks t...
Mamoon Ahmed Yahay Al Khadher· Iconic Research and Engineer...· 0 citations
Federated learning enables collaborative model training without centralizing private data. However, conventional aggregation strategies generally determine client contributions primarily according to local data volume and do not directly consider differences in local model performance. To address this limitation, we pr...
Emin Akpinar, M. Taşkıran, Bulent Bolat· Expert systems· 0 citations
Background . Oncology databases have become a foundation for developing models of tumor sensitivity to drug and radiation therapy; however, their clinical value is determined not by data volume but by the quality of the response phenotype, external validation, and evidence of treatment-effect modification. Objective. T...
Ilya Solovev, Denis Golubev· Real-World Data & Evidence· 0 citations
Federated learning (FL) has emerged as a transformative framework in artificial intelligence, addressing escalating concerns around data privacy, security, and distributed computation. Unlike conventional centralized learning systems that require data aggregation, FL enables collaborative model training across multiple...
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.
MIT News · Artificial Intelligence· news.mit.eduOct 6, 2026
