federated learning

Mixture-of-prompt experts for federated sentiment classification under non-IID data

Federated learning (FL) enables collaborative text classification while preserving data privacy, yet its performance can be degraded under non-IID data. This challenge is particularly severe in natural language processing, where client data may differ in linguistic style, topic distribution, and label semantics. Recent...

PromptGate: Client-Adaptive Vision–Language Gating for Open-Set Federated Active Learning

Rethinking Learning with Noisy Labels: A CriticalReview of Structured Supervision, RepresentationDynamics, and Robust-Learning Pipelines

Empowering Digital Agriculture: A Privacy-Preserving Framework for Data Sharing and Collaborative Research

Data-driven agriculture, which integrates technology and data into agricultural practices, has the potential to improve crop yield, disease resilience, and long-term soil health. However, privacy concerns, such as adverse pricing, discrimination, and resource manipulation, deter farmers from sharing data, as it can be...

Towards sustainable Federated Learning: A survey on energy and experimental practices

A Comprehensive Study on the Real-Time and Ethical Perspective of Integrating GeoAI for Multi-Hazard Disaster Risk Monitoring

This chapter by the authors introduces a practice oriented framework to the application of Geospatial Artificial Intelligence (GeoAI) to disaster risk intelligence and monitoring along the disaster management continuum. It explores the use of remote sensing, IoT and edge computing, machine learning, multi-hazard ensemb...

AI-Driven Intelligent IoT Frameworks for Early Neurocognitive Disorder Detection and Assistive Intervention

Neurocognitive disorders such as Alzheimer's disease, vascular dementia, and mild cognitive impairment (MCI) represent a growing global health crisis, affecting over 55 million people worldwide. Early and accurate detection is critical for timely therapeutic intervention. This chapter presents a validated, five-layer A...

Methods and applications of artificial intelligence in tuberculosis diagnostics: advances, opportunities, and challenges

Tuberculosis (TB) remains a leading global health challenge, with more than ten million cases annually and persistent diagnostic gaps that delay treatment and drive transmission. Despite advances in molecular assays and imaging technologies, conventional diagnostic tools, such as sputum microscopy, culture, and nucleic...

A secure federated learning framework with blockchain-based authentication for anomaly detection in cooperative smart farming

Agriculture is still a crucial global pillar of the world economy, ensuring food security and livelihood. But changing climate conditions and increasing reliance on the Internet of Things (IoT) based precision agriculture have brought new challenges, mainly cybersecurity. Cooperative Smart Farming (CSF) schemes promote...

AI-CARE: A Decision Support System Based On Federated Learning and Continuous Learning

Presentation given at the Virtual Physiological Human Conference in Milan 1-4 September 2026.

Digital-Twin-Coordinated Predictive Resource Management in 6G Cellular-Edge Networks Using Spatio-Temporal Graph Learning and Federated Reinforcement Learning

Federated Learning for Resource-Aware Intrusion Detection in IoT Networks: An Empirical Study of IID and Device-Level Non-IID Data

Official reproducibility package and research paper for:"Federated Learning for Resource-Aware Intrusion Detection in IoT Networks: An Empirical Study of IID and Device-Level Non-IID Data". ABSTRACT:The proliferation of Internet of Things (IoT) devices has expanded the network attack surface, increasing the risk of bot...