This paper presents a novel machine learning framework to consistently detect, localize and rate congenital cleft lip anomalies in human faces.
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.
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.
In this way, instead of just waiting for the slower clients to finish their computation, more clients can participate in each iteration.
The interconnection between different components in a power system causes failures to easily propagate across the system.
As a highly complex and integrated cyber-physical system, modern power grids are exposed to cyberattacks.
To the best of our knowledge, this is the first work based on GNN that automatically detects and localizes FDIA in power systems.
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.
Real-time traffic information can be utilized to enhance the efficiency of transportation networks by dynamically updating routing plans to mitigate traffic jams.
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.
Modern smart grids rely on advanced metering infrastructure (AMI) networks for monitoring and billing pur- poses.