federated learning

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

Trustworthy AI at the air quality-health nexus: a systematic review of federated learning, distributed ledger technologies, and explainable AI

The Green-AI-Trust Stack is proposed, a layered architectural framework that integrates three complementary pillars (FL for privacy-preserving distributed learning, DLT for verifiable data and model provenance, and XAI for interpretable inference and decision support within a unified AQI-health pipeline).

Digital Twin-Based Predictive Maintenance in 6G Smart Stadiums Using Standardized AI-Driven Models

The future of smart stadiums lies in highly connected, intricate systems that must respond instantly and maintain smooth operations. When it comes to handling the operational complexity of such dynamic infrastructures, traditional maintenance strategies — whether time-based or reactive — are rapidly falling short. A ga...

Physics-informed and topology-aware federated learning for transient stability assessment: A generative robust framework in heterogeneous power grids

Federated Aggregation of Polyhedral Demand Flexibility Sets

This paper proposes a federated framework for demand flexibility aggregation to support grid operations. Unlike existing geometric methods that rely on a static, pre-defined base set as the geometric template for aggregation, our framework establishes a true federated process by enabling the collaborative optimization...