Federated learning (FL) offers a promising approach to training machine learning models on decentralized data without directly exchanging the data itself. However, FL systems are vulnerable to Byzantine attacks, where malicious participants introduce corrupted data or models to compromise the learning process. This pap...
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
The Wisdom Continuum: A Unified Theory of Human Development, Learning, Intelligence, Artificial Intelligence, and Civilizational Evolution Description / Abstract The Wisdom Continuum presents a civilizational-scale framework that unifies human development, moral philosophy, educational reform, and artificial intelligen...
Wajid Hussain· 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 transportation sector accounts for approximately 21% of global CO 2 emissions ( ≈ 8.4 Gt CO 2 in 2024), and the electrification of road transport is a critical lever for decarbonisation. Annual global EV battery deployment reached 1.2 TWh in 2025, making accurate battery health monitoring essential for both perform...
Subrahmanyam Tanala, Appalabathula Venkatesh· Next Sustainability· 0 citations
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It is concluded that integrating FL with cross-chain blockchain technology can significantly enhance privacy, security, and collaboration among diverse AI systems, paving the way for next-generation decentralized intelligence.
Vikrant Thombare, Mahendra Sawane· International Journal of Cre...· 0 citations
A module-decomposition-driven SOC prediction model is proposed that separates baseline drain, synchronous state response, and asynchronous multi-scale event memory within one discharge-dynamics formulation and connects module-structured data processing, federated synthesis, few-shot adaptation, and local TTE inference...
This paper presents a novel approach to distributed machine learning, termed Federated Learning with Differential Privacy (FLDP), which addresses the critical challenge of privacy preservation in collaborative model training. Traditional federated learning techniques, while mitigating the risks associated with centrali...
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
The findings show that deep learning enhances aerial perception, reinforcement learning supports adaptive navigation and control, federated learning improves distributed intelligence, and swarm intelligence enables cooperative multi-UAV missions.
Binz A. Aziz, Mostafa A. Rushdi, Shigeo Yoshida et al.· Applied Sciences· 0 citations
This paper identifies key bottlenecks in technology deployment from a "Responsible AI" perspective and provides a theoretical framework and practical guidance for green AI and sustainable digital governance.
Zhen-Yu Lu· Advances in Engineering Inno...· 0 citations
Segmentation of medical images is a fundamental step towards quantification and clinical decision support. Many approaches have been proposed and successfully demonstrated on benchmark datasets. However, reliable application of current approaches in real clinical practice remains challenging. Medical image segmentation...
Sijia Zhu, Zhifang Sun, Juan Liao et al.· Discover Applied Sciences· 0 citations
The review combines recent research findings with guidance on new investigation tracks for developing all-encompassing AI systems which both spread far-reaching outcomes and follow sustainability requirements.
D. Usman· International Journal of Com...· 0 citations
Cloud computing has revolutionized the way computational resources are made available through scalable, flexible, and on-demand services. As promising as it is, efficient task scheduling remains an important challenge because of varied workloads, heterogeneous infrastructures, and multi-objective optimization requireme...
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
