federated learning

Deep Reinforcement Learning for Security-Aware Task Offloading in IoT Edge Computing: A Systematic Survey of Frameworks, Challenges, and Future Directions

Eight concrete research directions follow, from testbed validation and federated DRL to 5G channel modeling and grounding reward weights in real security-cost data.

Gozie01/ClinicalTrial.sol: Trust CT

Initial archived release of the TRUST-CT code and supporting materials for the federated learning, policy evaluation, security, and audit analyses.

A Privacy-Preserving Federated Data Exchange Architecture for Trusted Cross-Border Digital Services in the East African Community

The results show the potential of PP-FDEA for providing secure, privacy preserving, scalable and trusted EAC cross-border digital services.

World Scientific Committee for Planet Earth (WSCPE)

World Scientific Committee for Planet Earth (WSCPE) WSCPE proposes a new, standalone, voluntary and federated scientific and ethical coordination body for any technology with significant transboundary, systemic, planetary or intergenerational effects. It seeks verifiable and equitably distributed human progress under r...

A comprehensive survey of Edge-Fog-Cloud-IoT architectures: Intelligence, Security, and Sustainability

This survey provides an overview of Edge-Fog-Cloud-IoT architectural models and the technologies that have driven them during 2020–2026 and presents a holistic framework and a five-layer reference model that encompass the functional, performance, and trust aspects of distributed systems.

Blockchain-based secure provenance and auditability mechanism for medical IoT devices in healthcare ecosystems

The increasing approval of Internet of Medical Things (IoMT) in healthcare produces large volumes of sensitive patient data, raising critical challenges in security, privacy, and integrity due to weaknesses in centralized systems. Such systems are prone to tampering, unauthorized access, and a lack of transparency, gen...

Integrated Anomaly Detection and Mitigation in SDN Environments: A Hybrid Approach

A multilayer anomaly detection and mitigation framework for Software-Defined Networking environments consisting of four integrated subsystems that demonstrate quantifiable improvements over single-paradigm approaches in detection fidelity, response latency, resource efficiency, and privacy compliance.

Federated Learning with Explainable Artificial Intelligence for Privacy-Preserving Skin Cancer Detection: A Multi-Institutional Study

Quantum-Assisted Federated Learning Based Privacy Preserving Medical Image Classification

Developing a Federated, Lightweight, Interpretable, and Specialized AI-Based Intrusion Detection System for Medical Internet of Things (IoMT) Environments

The rapid expansion of the Internet of Medical Things (IoMT) has enhanced healthcare services, but it has also exposed these systems to a growing range of cyberattacks. Addressing this challenge requires effective intrusion detection solutions. Machine learning-based intrusion detection systems (IDSs) offer a promising...

SemioKAT-FL: explainable, background-invariant, and privacy-preserving video seizure detection with a Kolmogorov–Arnold Transformer and federated learning

Abstract Automatic detection of epileptic seizures from video promises non-invasive, continuous patient monitoring, yet web-sourced seizure corpora are dominated by background confounds : recording-specific cues (studio, home, clinic) that are spuriously correlated with the seizure label. Models that appear highly accu...

AI Governance Frameworks for Privacy-Preserving Intelligent Systems: Integrating Trust, Compliance, and Secure Data Architectures

The analysis suggests that governance frameworks succeed when privacy-preserving technologies are embedded into system architecture from the outset rather than added afterward, when compliance obligations are translated into measurable technical requirements, and when trust is treated as an emergent property of verifia...