federated learning

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

Privacy-Preserving Federated Learning for Cross-Border Data Flows in Uganda: Robustness Under Distribution Shift and Sparse Labels

Cross-border data flows are central to Uganda's digital economy, yet their governance is complicated by competing pressures: the need for international integration and the legal obligation to protect citizen privacy. Federated learning offers a promising architecture for training machine learning models across distribu...

Federated predictive load balancing for adaptive resource management in fog computing

A Federated Predictive Predictive Load Balancing (FPLB) framework is proposed to combine Long Short-Term Memory (LSTM) workload forecasting with federated learning, which does not require fog nodes to share their operational data.

IoT-Based Elderly Health Monitoring Information System with Federated Learning for Early Health Risk Prediction

The increasing elderly population in Indonesia has contributed to a higher prevalence of non-communicable diseases, including hypertension, cardiovascular disease, diabetes mellitus, and chronic obstructive pulmonary disease (COPD). This trend highlights the need for continuous, real-time health monitoring rather than...

Smart Cities AVNs: Transparent and Privacy-Preserving Cyber Attack Prediction with Explainable Federated Feature Selection