federated learning

FedCKA: Representation-Guided Layer Personalization for Federated 3D Perception Across Driving Domains

FedCKA is proposed, a Centered Kernel Alignment (CKA)-based strategy that dynamically handles the personalization-globalization trade-off, and outperforms established federated baselines, including FedBN, FedRep, and FedSelect, improving average NDS by 7 percentage points over the strongest baseline.

Enhancing the manufacturing processes and productivity in Industry 5.0 with the integration of machine learning and cyber-physical systems

This paper aims to examine the synergistic integration of Cyber-Physical Systems (CPS) and Machine Learning (ML) as a foundational enabler for Industry 5.0, focusing on creating human-centric, sustainable and resilient manufacturing ecosystems. The study utilizes a synthesis and review approach, analyzing re...

Federated Learning for Resilient Smart Grid Monitoring: A Physics-Based Simulation Framework for Distributed Energy Networks

Federated learning (FL) can support collaborative smart-grid monitoring without centralizing raw telemetry, but its value relative to local and centralized learning depends on both the operating regime and the learning model. This study evaluates that hypothesis in a physics-based AC power-flow simulation framework bui...

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

Abstract 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, susceptibilit...

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