Federated learning (FL) is a distributed machine learning (ML) approach that enables
models to be trained on client devices while ensuring the privacy of user data. Model …

Animal behavior, as one of the most crucial indicators of animal health and welfare, provides
rich insights into animal physical and mental states. Automated animal activity recognition …

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 …

Federated learning (FL), with the aim of training machine learning models using data and
computational resources on edge devices without sharing raw local data, is essential for …

Simple Summary Monitoring rumination behavior holds great potential as an objective
approach for assessing sheep health and well-being. This systematic review provides an …

Simple Summary This study aimed to improve animal activity recognition (AAR) using
wearable sensor data, which often faces challenges due to sensor variability and individual …

Precision agriculture has emerged as a vital solution to meet the food demands of the
growing global population. However, the high upfront costs of sensors, data analytics tools …

Automated animal activity recognition (AAR) has advanced greatly through recent advances
in sensing technologies and deep learning, and improved livestock management efficiency …

The extensive deployment of the Internet of Things (IoT) devices has highlighted significant
challenges related to data privacy, security, and communication bandwidth. Federated …

Research in the food domain is at times limited due to data sharing obstacles, such as data
ownership, privacy requirements, and regulations. While important, these obstacles can …