federated learning

Beyond Dirichlet Alpha: Realized Client Heterogeneity for Privacy–Utility–Fairness Evaluation in Federated Learning

Federated Learning in Underwater Sensor Networks

Underwater sensor networks (UWSNs) are essential for environmental monitoring, resource exploitation, disaster control, and maritime safety. Usage of machine learning (ML) algorithms within the networks, however, is not applicable due to high latency, low bandwidth, frequent topological changes, and privacy-stringent a...

Enhancing cybersecurity strategies for multicloud healthcare systems through deep learning-based threat detection and mitigation

QoE-Driven IoT Network Optimization using CNN,Transformer Attention and Federated Learning

Quality of Experience (QoE) has also become a user-centric performance metric in Internet of Things (IoT) networks, that is considered to be more realistic in reflecting user satisfaction than traditional Quality of Service (QoS) metrics. The current paper introduces a new hybrid model consisting of Convolutional Neura...

Hybrid Agile Method for Implementing Blockchain-Based Federated Learning in AI-Powered Universities

Universities are increasingly becoming AI powered organizations that rely on data driven decision making through the large scale processing and exchange of data. Compliance with data protection regulations, particularly the GDPR, means that the use of AI creates significant privacy and regulatory risks. To enable unive...

Decision-centered wearable biosensors for personalized rehabilitation: integrating multimodal monitoring, artificial intelligence, and closed-loop intervention

This critical narrative review synthesizes physiological, biochemical, electrophysiological, biomechanical, and behavioral sensing within a monitoring–interpretation–feedback–adaptation–outcome framework to help move wearable biosensing from fragmented technology demonstrations toward secure, personalized, continuous,...

Information Security and Federated Learning for Collaborative Healthcare Diagnosis

A federated learning framework with data locality for healthcare diagnostics that enables multiple hospitals to jointly train accurate models while keeping patient data within institutional boundaries is introduced.

ABE-FL: Efficient and secure federated learning based on CP-ABE with high-security elliptic curves

Existing secure federated learning (FL) schemes suffer from three major limitations: (1) the initial global model is unprotected during distribution; (2) multi-party computation incurs high communication overhead; and (3) the use of homomorphic encryption (HE) for parameter protection results in computational inefficie...

Deep Learning for Acoustic Signal Analysis

Acoustic signal analysis is essential to underwater monitoring, sonar imaging, and maritime security but suffers from persistent difficulties of high background noise, multipath propagation and limited labelled data. This chapter describes how recent developments in deep learning (DL) have overcome these limitations to...

A hierarchical federated multi-task transfer learning framework for paroxysmal arrhythmia detection, vital sign monitoring, and activity recognition

An internet of medical things (IoMT) enabled remote patient monitoring system needs to accurately detect heterogeneous physiological and activity signals while maintaining patient privacy and data flow constraints on sensitive information. Most previous federated or multi-task learning methods focus on one of the follo...

Federated Learning Applications for Cross Cultural Communication Analysis Through Privacy Preserving Integration of Linguistic and Paralinguistic Feature Extraction

Analysis of communication across cultures is essential in global digital interactions, requiring models that capture linguistic and paralinguistic features across diverse cultures. However, existing studies lack privacy-preserving frameworks that can effectively model both linguistic and paralinguistic features across...

Blockchain-assisted IoT-based smart electric vehicle network for secure data sharing and authentication

Experiments conducted in a controlled simulation environment show that BFGRL can effectively improve security, trust management, scalability, and operational efficienssssscy in blockchain-assisted smart EV networks.