The heterogeneity of data across Industrial Internet of Things (IIoT) devices poses significant challenges to federated learning-based intrusion detection systems, where the Non-IID data distribution leads to poor detection performance, particularly on rare attack classes. To address these issues, this letter proposes...
Xiaofei Huang, Fei Shu, Jaydar Jingus et al.· 0 citations
"This dataset contains Ultra-Wideband (UWB) ranging and localization measurements collected for the evaluation of adaptive and federated learning methods for indoor positioning in challenging environments. The data were acquired in a controlled two-room laboratory deployment designed to capture realistic line-of-sight...
Christos Sad, Kostas Siozios· IEEE DataPort· 0 citations
This narrative review provides radiologists with a balanced, comprehensive overview of digital twin architecture, advanced enabling technologies, current clinical evidence, a practical roadmap for implementation, and a candid appraisal of barriers to adoption.
Annamalai Vairavan, Rupsa Bhattacharjee, B. Raghavan· Indian Journal of Radiology...· 0 citations
Federated Learning (FL) offers a promising approach to training machine learning models on decentralized data while preserving data privacy. However, the inherent heterogeneity of data across clients and potential malicious behavior introduce challenges to the trustworthiness of FL systems. This work proposes a novel i...
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
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This paper presents a novel approach to collaborative model training leveraging federated learning, specifically designed for scenarios involving geographically dispersed data sources. The core challenge addressed is the 'data island' problem, where valuable data remains siloed due to logistical, regulatory, or competi...
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper presents a novel approach to differential privacy in federated learning, termed Dynamic Differential Privacy (DDP). The core idea is to adapt the privacy budget dynamically, responding to the varying sensitivity of local data. Traditional federated learning frequently utilizes a static privacy budget, which...
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper investigates the challenges and opportunities presented by federated learning in dynamic environments. Traditional federated learning approaches often rely on fixed network topology and data distribution, leading to suboptimal model performance and inefficient data utilization. We propose a novel self-adapti...
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
The automation of algorithmic discovery, a critical component of innovation across numerous fields, traditionally relies on the expertise of human researchers and engineers. This paper introduces a novel approach to algorithmic discovery leveraging self-aware distributed computing, specifically employing a federated le...
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper presents a novel approach to distributed learning leveraging Federated Bayesian Networks (FBNs). Traditional federated learning methods struggle with capturing complex, non-independent data distributions, often leading to suboptimal global models. We introduce a framework where multiple devices collaborative...
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
The automation of algorithmic discovery, a critical component of innovation across numerous fields, traditionally relies on the expertise of human researchers and engineers. This paper introduces a novel approach to algorithmic discovery leveraging self-aware distributed computing, specifically employing a federated le...
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Child abuse remains a critical global concern requiring efficient, secure, and scalable data management systems to support early detection and intervention. However, existing centralized approaches suffer from data fragmentation, limited interoperability, and significant privacy and security challenges. This paper prop...
Innocent Tom· INTERNATIONAL JOURNAL OF COM...· 0 citations
Starfish-FL is a general-purpose federated learning and analysis platform that orchestrates distributed computation across healthcare institutions without exposing patient-level data. Standardized statistical scripts execute locally behind each institution's firewall and only site-level aggregate outputs are shared for...
Yunkai Bao, Z.S. Saad, Kaue Duarte et al.· Zenodo (CERN European Organi...· 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.
MIT News · Artificial Intelligence· news.mit.eduOct 6, 2026
