Federated learning (FL) allows clinical institutions to train a shared model without pooling patient records, but itdoes not by itself prevent leakage through exchanged updates; because this study uses neither secure aggregationnor differential privacy, this exposure is discussed as a limitation. FL robustness under co...
SalmaZakzouk168, Sahar Selim, Lobna A. Said· Zenodo (CERN European Organi...· 0 citations
Fake news detection is the process of detecting misinformative or crafted content spread through social media. Fake news causes social instabilities, increases the data leakage risks, and affects the user's privacy. The prevailing fake news detection models failed to tackle certain challenges, including overfitting iss...
P Priyanga, Pravin R. Kshirsagar, Sivaneasan Bala Krishnan et al.· Scientific Reports· 0 citations
Federated Learning (FL) enables heterogeneous clients to collaboratively train a shared model while keeping raw data local. In practical FL systems, client participation is often constrained by limited battery energy, and long-term unselection of clients may lower their willingness to participate, which further reduces...
Siwei Xue, Gang Li· Proceedings of the 55th Inte...· 0 citations
A decentralised federated learning (FL)-based IoT malware detection framework, evaluated using the recent IoT-23 dataset and systematically assessed in terms of robustness and scalability, highlighting the feasibility of robust and scalable FL-based security systems in real-world IoT deployments.
Saba Nayab, Sana Qadir, Madiha Khalid et al.· Journal of Computer Virology...· 0 citations
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An Internet of Things-based, privacy-preserving Federated Learning (FL) framework for predicting machine failures in Industry 5.0 is proposed, addressing the frequently neglected concerns of data privacy and decentralized operational settings.
Shriya Seth, Harshpreet Singh, Suhasini Monga et al.· Journal of Quality in Mainte...· 0 citations
The authors discussed the potential of federated learning in intelligent data analytics with privacy, and reported the potential advantages of federated learning in the context of secure and intelligent data analysis, while preserving data privacy.
Sonam Ashok Rewatkar, Anil Ramdas Khuje, Nusrat Khan et al.· International Journal of Eng...· 0 citations
The findings suggest the feasibility of Federated Learning for developing privacy-preserving and highly generalizable OMR systems for large-scale educational assessment.
The rapid growth of solar photovoltaic systems has exposed persistent challenges in their operation reliability, performance forecasting and integration with power systems. Therefore, accurate assessment of solar photovoltaic system behaviour and on-field reliability are required through comprehensive modelling to en...
A. Banerjee, Bernd Szyszka, Sanket Goel et al.· Clean Energy· 0 citations
FedMediFormer-XAI is introduced, a unified and proper framework that incorporates federated learning, multimodal transformers, diffusion-based data augmentation, Graph Neural Networks for drug recommendation, and Explainable Artificial Intelligence (XAI) for diabetes intelligence.
Unknown authors· Journal of Visualized Experi...· 0 citations
Overall, the proposed framework addresses three critical research gaps: preserving data privacy without centralized data aggregation, handling non-IID data distributions in IoT networks, and enabling efficient computation for resource-limited devices.
Baraa I. Farhan· Al-Noor Journal of Engineeri...· 0 citations
Artificial Intelligence (AI) is reshaping education by enabling personalized content delivery tailored to individual learner profiles. This transformation enhances learner engagement, knowledge retention, and academic success. Personalized learning addresses the diverse needs of students through intelligent algorithms...
Deepti Sharma, Archana B.Saxena· Journal of Asia Entrepreneur...· 0 citations
Overall, FedMVC represents a promising framework for privacy-preserving unsupervised learning in distributed environments and key research challenges include formal privacy–utility trade-offs, convergence under extreme data heterogeneity, and the integration of large language models for improved interpretability.
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
