federated learning

A Blockchain-Federated Learning Framework for Privacy-Preserving Medical Image Analysis with Adaptive Differential Privacy and Zero-Knowledge Proof Aggregation

Federated learning with blockchain technology is a promising domains to facilitate privacy-sensitive collaborative intelligence in the domain of medical imaging. Traditional federated learning systems are affected by several classes of vulnerabilities, including dependence on a single aggregator, susceptibility to gr...

Lifelong-IDS

Federated Learning (FL) enables collaborative intrusion detection across distributed Internet of Things (IoT) edge devices without centralizing private network telemetry. However, existing frameworks assume static data distributions and fail under non-stationary conditions: emerging zero-day attack families cause sever...

Digital Veterinary Care in Companion Animal Practices: Integrating Telemedicine, Artificial Intelligence, and Remote Patient Monitoring

Digital technologies are reshaping companion-animal healthcare by expanding access to veterinary services and enabling more connected, data-driven models of care. This narrative review synthesizes current evidence on digital veterinary care, with a focus on the integration of telemedicine, artificial intelligence (AI),...

Integrating Blockchain with AI: A Data-Driven Model for Secure, Scalable Decentralized Finance (DeFi) Systems

The rapid evolution of decentralized finance (DeFi) has ushered in transformative opportunities for global financial inclusion, digital asset management, and permissionless transactions. However, the growth of DeFi also presents significant challenges related to data integrity, security, scalability, and regulatory com...

Artificial Intelligence for Air Quality Prediction: A Review of Machine Learning and Deep Learning Applications in Atmospheric Pollution Forecasting

Air pollution is a major global environmental and public health challenge, causing respiratory diseases, environmental degradation, and premature mortality. Accurate air quality prediction is essential for environmental management, reducing human exposure to harmful pollutants, and supporting timely mitigation strategi...

A systematic review of machine learning and deep learning methods for COVID-19 diagnosis, prognosis, and outbreak forecasting

The COVID-19 pandemic accelerated the development of artificial intelligence (AI), machine learning (ML), and deep learning (DL) for clinical decision support, public-health surveillance, and pandemic response. This systematic review evaluates AI-based studies addressing COVID-19 diagnosis, prognosis, outbreak forecast...

Improving diagnosis and interpretability in chest X-rays through federated learning and adversarial training

Chest X-ray imaging is the most common radiological test for diagnosing a wide range of lung conditions. Deep Learning (DL) has emerged as a powerful tool for automated analysis of chest X-rays, but it requires large, well-annotated datasets that are often difficult to obtain due to privacy concerns and instituti...

Byzantine-Robust Federated Learning

Constraint-Guided Parameter-Efficient Fine-Tuning Personalized Federated Learning with Adaptive Gaussian Differential Privacy and Conformal Calibration

FLInterrupt: An interactive federated learning simulator for client interruption experiments

We introduce FLInterrupt, an open-source, browser-based federated learning (FL) simulator for research on involuntary client interruption during synchronous FedAvg. The backend runs communication rounds in PyTorch. A React dashboard lets researchers set datasets and models, interrupt or reconnect clients at runtime, re...

Practical Byzantine-Resilient Federated Learning via Zero-Knowledge Proofs and Adaptive Reputation

Trust management under adversarial uncertainty is a central challenge in distributed learning systems. We propose Verifiable FL with Two-Stage Selection (VFL-TS), a knowledge-driven framework for Byzantine-resilient federated learning that maintains a dynamic trust knowledge base—updated through cryptographically verif...

Lifelong-IDS

Federated Learning (FL) enables collaborative intrusion detection across distributed Internet of Things (IoT) edge devices without centralizing private network telemetry. However, existing frameworks assume static data distributions and fail under non-stationary conditions: emerging zero-day attack families cause sever...