federated learning

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...

Artificial Intelligence-Empowered “Walking Hospitals”: A Narrative Review of Innovative Models and Ecosystem Construction in Rural Healthcare

An AI-empowered “Walking Hospitals” represent a paradigm shift from hardware distribution to capability enhancement, and future research must prioritize resolving domain shift through techniques like Federated Learning, establishing sustainable reimbursement models, and developing community-level data governance framew...

Fisher-guided personalized federated learning with efficient packed homomorphic aggregation

A closed-loop paradigm for precision oncology: Integrating dynamic monitoring, organoid models, and explainable AI.

BACKGROUND Precision oncology faces three systemic bottlenecks: static molecular snapshots fail to capture tumor dynamics, patient‑derived organoids lack physiological reconstruction of the tumor microenvironment (TME), and artificial intelligence (AI) remains a "black box" in clinical decision‑making. This review aims...

Federated Edge Learning for Intelligent IoT Systems

Privacy-preserving federated learning with MobileViT for chest X-ray classification in Nigerian hospitals

Nigeria faces challenges in the accurate diagnosis of respiratory diseases such as tuberculosis (TB), pneumonia, and COVID-19 because of limited radiology resources and stringent patient-privacy requirements. Deep learning models can provide strong diagnostic performance but often rely on centralised data, raising ethi...

Decentralized Federated Learning with Byzantine Fault Tolerance and Differential Privacy via Blockchain

Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources without directly exchanging data. However, existing FL systems are vulnerable to various attacks, including model poisoning and Byzantine attacks, which can compromise model integrity and user privacy....

AI-based context-aware data anonymization with federated adaptive differential privacy for IoT applications

A novel data anonymization framework based on federated learning and adaptive differential privacy for privacy-preserving IoT healthcare data management while achieving an improved balance between data utility, computational efficiency, and privacy protection is proposed.

FL-Forensics: A Blockchain-Enabled Framework for Forensic Accountability and Auditability in Federated Learning