federated learning

Structure-aware federated hypergraph continual learning

FedSHARP (Federated Structure-aware Hypergraph Anchored Representation Preservation), a structure-aware framework for federated hypergraph continual learning, is proposed and evaluated, demonstrating that preserving both node-level semantics and high-order hyperedge structures is important for federated hypergraph cont...

Differential Privacy in Feature Reconstruction Aided Federated Learning for Agent's Semantic Communication Model Update

Experimental results on multiple datasets show that the proposed DP-aided FedSFR outperforms DP-enabled FedAvg in training stability and image reconstruction quality in heterogeneous wireless systems.

Machine Learning Applications In Diesel Engine Emissions Control: A Systematic Review of Predictive Modeling, Adaptive Control, and Real-Time Optimization Strategies (2010–2024)

The reviewed literature reveals four generational phases of ML adoption in diesel emissions control, from simple regression-based offline calibration tools to sophisticated real-time physics-informed adaptive systems, which consistently outperform purely data-driven approaches on the out-of-distribution conditions most...

Federated learning and AI-based ZTA for security, privacy, telemetry, IAM and fileless malware detection and prevention framework: an in-depth review

This comprehensive review delves into integration of the FL with the AI-based ZTA for the malware detection and Prevention and presents the comparative analysis between the AI, FL, ZTA, IAM approaches, and the trends, challenges, limitations, and the future directions.

Blockchain for Security and Privacy in AI-Based Education: A Systematic Literature Review

It is recommended that educational institutions and Ed-Tech developers transition to a hybrid storage architecture and the results of the empirical analysis show that centralized databases are highly vulnerable to Single Points of Failure (SPOF).

SAFORI‐IDS: Semantic‐Aware Federated Intrusion Detection for IoT‐Enabled 6G Metaverse Infrastructure

The original SAFORI‐IDS framework uniquely merges semantic context and federated privacy to secure 6G‐enabled Metaverse environments and achieves over 99% accuracy, precision, recall, and F1‐score, significantly outperforming state‐of‐the‐art baselines while maintaining lightweight computational efficiency suitable for...

Ultra-low communication overhead federated learning strategy for distributed wearable ECG networks

ECG-FedCS is proposed, a hardware-aware FL framework that dynamically modulates gradient sparsification intensity based on real-time ECG signal quality, QRS complex morphology, and heart-rate variability indices extracted on-device, establishing the first FL framework to achieve this Pareto improvement in a multi-proto...

Development of an Enhanced Federated Feedforward Deep Learning Framework for Semantic Communication in Cross Institutional Research Collaboration

Compared to CNN- and GNN-based methods, the results show that the suggested semantic enabled FFNN framework provides a better balance of accuracy, communication efficiency, scalability, and privacy preservation, making it a workable and efficient solution for safe, extensive, cross-institutional research collaboration...

Artificial Intelligence-Powered Histopathology in Stem Cell Research: Bridging Morphology, Function, and Omics

The histopathology area is being redefined with the use of artificial intelligence (AI), providing robust tools to help bridge cellular morphology, functional assays, and multi-omics data in stem cell research and providing new possibilities for precision medicine, personalized therapies, and translational uses like dr...

Lightweight AI for UAV-Mounted RIS: An Overview

A comprehensive overview of lightweight AI techniques for UAV-mounted RIS systems, including Reinforcement Learning (RL), meta-learning, meta-learning, Federated Learning (FL), Multi-Armed Bandits (MAB), and energy-aware optimization are provided.

FL-BC-IDS: Evidence-Native Privacy-Aware Hierarchical Federated Intrusion Detection for the Internet of Vehicles

FL-BC-IDS is presented, an evidence-native, privacy-aware hierarchical federated IDS in which vehicles train Differentially Private XGBoost models, roadside units perform deterministic admission and tree-bagging aggregation, and the GLOBAL stage forms an equal-weight ensemble over validated RSU models.

Temporal deep reinforcement learning with federated explainable AI for longitudinal cardiovascular risk prediction and personalized intervention recommendation