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Found 13 papers, 1 papers with code
CleftGAN: Adapting A Style-Based Generative Adversarial Network To Create Images Depicting Cleft Lip Deformity
We undertook a transfer learning protocol testing different versions of StyleGAN-ADA (a generative adversarial network image generator incorporating adaptive data augmentation (ADA)) as the base model.
Federated Learning Based Distributed Localization of False Data Injection Attacks on Smart Grids
By employing federated learning for the detection of FDIA attacks, it is possible to train a model for the detection and localization of the attacks while preserving the privacy of sensitive user data.
Unsupervised Anomaly Appraisal of Cleft Faces Using a StyleGAN2-based Model Adaptation Technique
This paper presents a novel machine learning framework to consistently detect, localize and rate congenital cleft lip anomalies in human faces.
Multi-Task and Transfer Learning for Federated Learning Applications
So, supporting federated learning with meta-learning tools such as multi-task learning and transfer learning will help enlarge the set of potential applications of federated learning by letting clients of different but related tasks share task-agnostic models that can be then further updated and tailored by each individual client for its particular task.
Infinite Impulse Response Graph Neural Networks for Cyberattack Localization in Smart Grids
This study employs Infinite Impulse Response (IIR) Graph Neural Networks (GNN) to efficiently model the inherent graph network structure of the smart grid data to address the cyberattack localization problem.
Clustered Scheduling and Communication Pipelining For Efficient Resource Management Of Wireless Federated Learning
In this way, instead of just waiting for the slower clients to finish their computation, more clients can participate in each iteration.
Spatio-Temporal Failure Propagation in Cyber-Physical Power Systems
The interconnection between different components in a power system causes failures to easily propagate across the system.
Cyberattack Detection in Large-Scale Smart Grids using Chebyshev Graph Convolutional Networks
As a highly complex and integrated cyber-physical system, modern power grids are exposed to cyberattacks.
Joint Detection and Localization of Stealth False Data Injection Attacks in Smart Grids using Graph Neural Networks
To the best of our knowledge, this is the first work based on GNN that automatically detects and localizes FDIA in power systems.
Graph Neural Networks Based Detection of Stealth False Data Injection Attacks in Smart Grids
False data injection attacks (FDIAs) represent a major class of attacks that aim to break the integrity of measurements by injecting false data into the smart metering devices in power grids.
Utility of Traffic Information in Dynamic Routing: Is Sharing Information Always Useful?
Real-time traffic information can be utilized to enhance the efficiency of transportation networks by dynamically updating routing plans to mitigate traffic jams.
Multiagent Systems
Hybrid RF/VLC Systems: A Comprehensive Survey on Network Topologies, Performance Analyses, Applications, and Future Directions
In parallel, visible light communication (VLC) has been proposed as an alternative solution, where a light source is used for both illumination and data transmission.
Deep Recurrent Electricity Theft Detection in AMI Networks with Random Tuning of Hyper-parameters
Modern smart grids rely on advanced metering infrastructure (AMI) networks for monitoring and billing pur- poses.
