federated learning

Transformer-Based Multimodal Image Fusion in Healthcare: A Comprehensive Survey of Architectures, Fusion Strategies, and Clinical Applications

Transformer architectures have changed how multimodal medical image fusion works. Convolutional neural network (CNN)-based methods are constrained to local receptive fields; transformers use self-attention and cross-modal interactions to capture dependencies across the full image, which matters when aligning magnetic r...

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

Personalized rehabilitation requires repeated assessment of movement, physiological tolerance, fatigue, and adherence, yet conventional clinic-based measurements provide only intermittent snapshots. The convergence of wearable biosensors with artificial intelligence, edge-cloud computing, and connected data infrastruct...

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...