federated learning

Federated learning and autoencoder-based hybrid framework for DDoS detection

Abstract Distributed Denial-of-Service (DDoS) attacks can overwhelm network resources and disrupt the availability of cloud services, enterprise systems, and critical infrastructure. Machine-learning-based intrusion detection can help identify malicious traffic from network-flow characteristics. However, conventional c...

Cross-agency financial fraud detection based on federated graph neural networks and privacy protection

This method constructs individual financial graphs using accounts, invoices, devices, terminals, and transaction relationships, and employs federated secure aggregation for joint modeling across organizations to address the discretization, isolation, and privacy protection issues in cross-organizational financial fraud...

Learning to Route On-Chip: A Survey of Machine Learning-Based Routing in Networks-on-Chip and Its Hardware Overheads

This survey reviews machine-learning-based NoC routing across a 48-study evidence base, and identifies three recurring gaps: limited scalability beyond small meshes, missing deadlock-freedom guarantees for learned policies, and over-reliance on synthetic traffic.

Comprehensive Review on Artificial Intelligence-Based Smart Healthcare Monitoring Using IoT and HCI

This review critically examines the integration of AI, IoT, and HCI in smart healthcare monitoring and identifies unresolved challenges involving dataset bias, sensor reliability, model drift, human over-reliance, alarm fatigue, interoperability, regulatory compliance, and equitable access.

POCS: phased optimal client selection for federated learning

Abstract Traditional federated learning suffers from excessive communication overhead due to iterative model exchanges, long training time caused by data heterogeneity, and vulnerability to adversarial attacks due to gradient leakage risks. But if one strategically selects some specific clients for training during diff...

TyMill/FedGridSim: v1.2.0

FedGridSim v1.2.0 — Reviewer Revision Release Overview FedGridSim v1.2.0 is the reproducibility release accompanying the revised manuscript: "Federated Learning for Resilient Smart-Grid Monitoring: A Physics-Based Simulation Framework for Distributed Energy Networks." The release consolidates the simulation, federated-...

Explainable Federated Learning for Trustworthy Thoracic Disease Detection Under Non-IID Data Distributions

Comparison of SHAP and Grad-CAM attribution maps confirms clinically coherent disease-specific localisation, and reveals monotonic performance degradation, identifying minimal regularisation as optimal for multi-label medical imaging.

PFL-AP: Personalized Federated Learning With Adaptive Affine Calibration and Prototype-Driven Knowledge Fusion for Non-IID Data

Personalized federated learning (PFL) based on model decoupling has emerged as a prominent paradigm for mitigating statistical heterogeneity. However, most existing methods rely on static or oversimplified local representations, neglecting the severe feature misalignment during global aggregation. This inevitably preci...

Multicamera Connected Vision System With Multiview Analytics: A Comprehensive Survey

Connected vision systems (CVSs) are transforming a variety of applications, including autonomous vehicles, smart cities, surveillance, and human–robot interaction. These systems harness multiview multicamera (MVMC) data to provide enhanced situational awareness through the integration of MVMC tracking, re-identificatio...

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...

Optimized explainable deep learning and federated learning-based QoS multicast tree routing in MANETS with CF-mMIMO

StabFL: Stabilized Logit Adjustment with Queue-Augmented Representation Learning for Label-Skewed Federated Learning

Federated learning under label skew aggregates models trained with different local class distributions. Local logit adjustment can reduce class-dependent bias, but the adjusted loss updates both the encoder and classifier in client-specific directions. StabFL optimizes a joint local objective that combines logit-adjust...