federated learning

Fuzzy Delphi-based Assessment of Privacy-Aware Healthcare Monitoring Architectures in Remote and Resource-Constrained Areas

Intelligent healthcare monitoring in geographically isolated communities requires solutions that can operate under limited digital infrastructure while maintaining privacy, regulatory compatibility, and public confidence. Selecting an appropriate system architecture is therefore particularly important in areas where co...

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...

Advanced Technologies for Sustainable and Effective Waste Management Systems

Rapid urbanization, industrialization, and population growth have led to a significant rise in wastewater and solid waste generation, posing critical environmental and public health challenges worldwide. Conventional waste management systems, largely designed for disposal, are no longer sufficient to meet the demands...

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.