federated learning

A federated learning-enabled distributed optimal control mechanism for energy supply chains

A distributed collaborative optimization control strategy mechanism for microgrid clusters based on a federated learning architecture is designed, coupling a proximal regularization dimensionality reduction operator with an adaptive attention aggregation alignment algorithm based on the high-dimensional space cosine an...

Wearable AI Architecture for Continuous Health Monitoring

Wearable artificial intelligence (AI) has emerged as a transformative technology for continuous health monitoring, enabling real-time physiological sensing, intelligent data interpretation, and proactive healthcare delivery. This chapter presents a comprehensive overview of wearable AI architecture, focusing on the int...

FedFD: Semantics-Guided Feature Diffusion for Federated Learning with Private Extractors

Federated Learning in Finger Biometrics: Opportunities and Challenges

Lightweight And Privacy-Preserving Intrusion Detection For The Internet Of Things: A Systematic Review Of Datasets, Models, And Emerging Directions

The rapid expansion of Internet of Things (IoT) deployments has been matched by a corresponding rise in device- and network-targeted attacks, while the limited computation, memory, and energy budgets of most IoT endpoints make conventional, resource-intensive intrusion detection systems (IDS) difficult to deploy direct...

Frequency-guided adaptive privacy masking network

Medical image sharing and collaborative analysis have become essential for advancing diagnostic capabilities across healthcare institutions, yet they introduce substantial privacy risks when sensitive patient information is exposed during transmission or processing. Existing approaches to privacy preservation in medica...

Dynamic federated multi-agent planning under partial observability and network instability for real-time edge applications

In recent years, the proliferation of autonomous systems at the edge—including robotic swarms, autonomous vehicles, and intelligent sensor networks—has highlighted the urgent need for resilient multi-agent planning under uncertain, decentralized conditions. Traditional multi-agent planning architectures typically assum...

Collaborative Federated Learning to Secure 6G-IoT with Deep Convolutional Generative Adversarial Network

A distributed IDS-based collaborative FL (IDS-CFL) across different learning levels: device and fog-cloud, to reduce data transfer and improve accuracy and enable fast processing is proposed, using a Deep Convolutional Generative Adversarial Network model to train data at each level.

Edge and Cloud Computing in Wearable AI Systems

The swift evolution of wearable artificial intelligence (AI) systems has significantly impacted continuous health monitoring, personalized healthcare, and human–machine interaction. At the heart of this revolution is the complementary convergence of edge and cloud computing models, which together facilitate scalable, r...

The Cheapest Digital Twin: A Pre-Registered Map of Twin-Native Extensions to a Frozen Single-Token Edge Encoder

The Cheapest Digital Twin: A Pre-Registered Map of Twin-Native Extensions to a Frozen Single-Token Edge Encoder Randolph James Ferlic, M.D. and Kimberly Kate Ferlic — Fieldstone Analytics, LLC, Austin, TX, USA Preprint · Zenodo DOI: 10.5281/zenodo.22922678 · CC-BY 4.0 · Community: spiral-domain-encoder-campaign Abstrac...

Adaptive personalized over-the-air federated learning with reflecting intelligent surfaces

Over-the-air federated learning (OTA-FL) unifies communication and model aggregation by leveraging the superposition property of the wireless medium, enabling bandwidth-efficient learning via simultaneous transmission of model updates. In this paper, we consider a federated learning system over a heterogeneous edge net...

Heterogeneity‐Aware Federated Learning via Heterogeneity Identification and Adaptive Optimization

This work proposes an integrated framework that combines parameter‐oriented heterogeneity identification with adaptive optimization, and introduces an attention‐guided prior loss that imposes stronger constraints on heterogeneity‐sensitive parameter regions while preserving local adaptability in less sensitive regions.