Federated learning offers a compelling architecture for peacebuilding analytics in conflict-affected regions, where data sovereignty, institutional sensitivity, and infrastructural fragility preclude centralised data aggregation. We introduce a federated empirical risk minimisation objective regularised by a Wasserstei...
Achieng Mary Goretti Odongo, Nassiwa Brenda Tumusiime, Mugisha Brian Byamukama· Zenodo (CERN European Organi...· 0 citations
Reliable marine-engine fault diagnosis in maritime IoT is challenged by distributed data ownership, heterogeneous fault distributions, and continuously changing operating conditions. This paper proposes SeaCausal-FL, a federated fuzzy causal learning framework that combines a shared temporal diagnostic path with mechan...
Yuhang Qiu, Haihan Zhu, Koteeswaran Seerangan et al.· 0 citations
Start learning: Bahasa Indonesia · English interface. Version 0.63.22 applies reciprocal learner navigation across the complete hosted program surface: every reader links to its course and localized program, every course links to its readers, and hosted copies retain prominent authoritative-original routes. Current lan...
KokunoYumeto, OpenAI Codex· Zenodo (CERN European Organi...· 0 citations
Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources without directly exchanging data. However, a key challenge in the widespread adoption of FL is the incentive problem: motivating participants to contribute their valuable data and computational resource...
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
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This paper proposes a novel decentralized federated learning (DFL) system designed to address the critical challenges of privacy and accountability inherent in collaborative learning environments. The system leverages the strengths of three key technologies: decentralized learning, differential privacy, and blockchain...
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources without directly sharing the data itself. However, existing FL systems face significant vulnerabilities, including malicious participant attacks and data breaches. This paper proposes a novel decentrali...
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Federated learning offers a compelling architecture for training machine learning models across distributed Kubernetes clusters without centralising sensitive data, yet its practical deployment in resource-constrained settings such as Uganda confronts two interrelated obstacles that current literature treats in isolati...
Federated Learning (FL) offers a promising approach to training machine learning models on decentralized data sources without direct data sharing. However, the inherent lack of transparency in FL raises concerns about trust, fairness, and model reliability. This paper explores the critical need for explainable AI (XAI)...
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Human Activity Recognition (HAR) systems deployed on wearable and edge devices continuously collect sensitive user data, raising critical privacy concerns and communication bottlenecks. While Federated Learning (FL) mitigates these issues by decentralizing model training and keeping raw data on-device, deploying highly...
Mohammad Ariaeimehr· Zenodo (CERN European Organi...· 0 citations
Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources, such as Internet of Things (IoT) devices, without directly exchanging raw data. However, the practical deployment of FL in IoT environments is hindered by several key challenges, including significant...
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources without directly exchanging data. However, traditional differential privacy (DP) techniques, designed for centralized learning, often lead to substantial accuracy degradation in FL settings due to the i...
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Federated learning (FL) presents a promising paradigm for training machine learning models on decentralized data sources without directly exchanging the data itself. However, achieving differential privacy (DP) – a rigorous privacy guarantee – within the FL setting remains a significant challenge due to the computation...
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
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
