Federated learning has emerged as a promising approach for collaborative model training
across distributed devices. Federated learning faces challenges such as Non-Independent …

Simple Summary This study addresses the classification task in animal science, which helps
organize and analyze complex data, essential for making informed decisions. It introduces …

Abstract In Federated Learning (FL), a shared model is learned across dispersive clients
each of which often has small and heterogeneous data. As such, datasets in FL setting may …

Anomaly detection is an important function in IoT applications for finding outliers caused by
abnormal events. Anomaly detection sometimes comes with high-frequency data sampling …

Personalized Federated Learning (PFL) tools have been recently applied in Anomaly
Detection (AD) setups to accurately monitor complex industrial systems under data het …

When building a performing one-class classifier, the low variance direction of the training
data set might provide important information. The low variance direction of the training data …

The pervasivity of artificial intelligence is structurally changing how societies envision their
development. The perception of what is valuable is rapidly shifting, and data is in the eye of …

The fast-growing development of smart home environments and the popularity of IoT
devices have increasingly raised security concerns about anomalous behaviour and events …

We propose to leverage the WiFi fingerprint of people in confined areas to monitor and
manage the mobility of the crowd in a smart city. We transform the indoor positioning …

Consider a graph G where each vertex has a threshold. A vertex v in G is activated if the
number of active vertices adjacent to v is at least as many as its threshold. A vertex subset A …