federated learning

AI for Surface Water Quality Prediction: A Comprehensive Review

Artificial intelligence for surface water quality prediction has evolved rapidly from classical regression and ensemble learning toward deep sequence, graph-based, physics-informed, and representation-learning approaches, while a largely disconnected systems literature has matured around digital twins, edge intelligenc...

Modality-aware differentially private federated learning for partially observed multimodal healthcare prediction

Multimodal healthcare prediction can benefit from combining structured electronic health records, medical imaging, and acquisition-related metadata, but these modalities are often incompletely observed across patients and unevenly distributed across clinical sites. Federated learning provides a mechanism for collaborat...

A Privacy-Preserving Federated Grey Wolf Optimization and Blockchain Framework for Trust Management in Decentralized 6G Edge Networks

Robust Federated Learning for Intensive Care Unit Mortality Prediction Across a Heterogeneous Hospital Network

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...

Distributed learning-enabled federated large language model for fake news detection

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...

SCPFL: A Safety Reinforcement Learning Framework for Joint Energy-Fairness Optimization in Long-term Federated Learning

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...

Enhanced IoT malware detection via robust and scalable federated learning

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.

An IoT-based privacy-preserving federated learning framework for proactive machine maintenance prediction in Industry 5.0

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.

FEDERATED LEARNING-BASED PRIVACY PRESERVATION FOR INTELLIGENT DATA ANALYTICS

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.

Federated OMR for Automated Examination Paper Digitization and Assessment

The findings suggest the feasibility of Federated Learning for developing privacy-preserving and highly generalizable OMR systems for large-scale educational assessment.

Solar Photovoltaic system design with Digital-Twin approach and its challenges with possible mitigations: A comprehensive review

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...

FedMediFormer-XAI: Federated Multimodal Transformers with Diffusion Augmentation and Graph-Based Drug Recommendation for Diabetes.

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.