federated learning

Cloud Robotics and Edge Intelligence: Distributed Control, Latency Modeling, and Fleet Coordination

An open-access curriculum module exploring the architectural frameworks, communication bounds, and machine learning topologies that govern distributed robotics. Covers three-tier cyber-physical hierarchies (embedded edge devices, local fog/edge servers, centralized hyper-scale cloud), non-minimum phase transfer functio...

Cloud Robotics and Edge Intelligence: Distributed Control, Latency Modeling, and Fleet Coordination

An open-access curriculum module exploring the architectural frameworks, communication bounds, and machine learning topologies that govern distributed robotics. Covers three-tier cyber-physical hierarchies (embedded edge devices, local fog/edge servers, centralized hyper-scale cloud), non-minimum phase transfer functio...

FedMuse: A Privacy-Aware Federated Framework for Multi-Style Text-to-Image Art Generation

FedMuse is proposed, a privacy-aware federated framework for multi-style text-to-image art generation, in which distributed clients collaboratively train a shared generative model while keeping their private art data local, and an adaptive privacy regularizer is introduced to reduce memorization risk while maintaining...

Artificial Intelligence-Enabled Low-Carbon Transition in Shipping: A Systematic Bibliometric Review

An evidence-informed AI-to-Carbon Value Chain conceptual synthesis comprising data observability, model credibility, decision executability, system coordination, and carbon verification is advanced, although this synthesis is interpretive rather than a validated causal framework.

Heavy data and AI updates stay off-chain, while a permissioned blockchain stores critical proofs, identity, authorization, roaming records, slice events, and audit trails. This builds trust between operators and supports AI/federated learning. Main tradeoffs—latency, scalability, privacy, and decentralization—are handled with hybrid on/off-chain designs.

Deep Learning, Federated Learning, and Meta-Learning for Intrusion Detection in the Internet of Things: A Systematic Review, Taxonomy, and Research Agenda

Auditable update-utility certificates for personalized federated learning

Reliability metadata improve federated aggregation when they predict the value of a client update for other participants when they predict the value of a client update for other participants using auditable cross-client update-utility certificates.

HAFN: a federated learning framework for privacy-preserving stress detection

This study proposes a federated learning framework, the Hierarchical Attention Fusion Network (HAFN) combined with the FedNova aggregation strategy for privacy-preserving multimodal stress classification, and demonstrates robustness and effectiveness of the proposed framework.

Artificial Intelligence and Machine Learning Models in the Prediction, Analysis, and Treatment Support of Rotavirus and Pediatric Diarrhea: A Systematic Review (2020- 2025)

Rotavirus is a viral infection that affect mostly children below 5 years of age. It remains a leading cause of acute gastroenteritis and diarrheal morbidity among children under five years globally, despite vaccine availability. It causes inflammation of the digestive traits which results in diarrhea, vomiting and feve...

CUST-Iris

The CUST-Iris dataset was collected at Capital University of Science and Technology (CUST), Islamabad, Pakistan, for biometric iris recognition research. The dataset contains 2,880 iris images acquired from 720 unique irises (L+R) using the Crossmatch I-Scan 2 iris scanner. Images were captured in the near-infrared (NI...

A distributed supply chain inventory collaborative replenishment strategy based on edge-agent federated learning

With the continuous expansion of the supply chain scale and the widespread adoption of distributed architectures, how to achieve cross-node inventory collaborative optimization while protecting the data privacy of all participants has become a key challenge. To address this challenge, this paper proposes a distributed...

Artificial Intelligence for Early Prediction and Diagnosis of Neonatal Sepsis: Current Evidence, Challenges, and Future Directions

Neonatal sepsis remains a major cause of morbidity and mortality worldwide, while timely diagnosis continues to be challenging because of nonspecific clinical manifestations and limitations of conventional diagnostic methods. Recent advances in artificial intelligence (AI) have created new opportunities for the early p...