Federated learning has recently emerged as a privacy-preserving distributed machine
learning approach. Federated learning enables collaborative training of multiple clients and …

The proliferation of data silos poses a significant impediment to the advancement of
machine learning applications. The traditional approach of centralized data learning is …

Under the federated learning paradigm, the agents learn in parallel and combine their
knowledge to build a global knowledge model. This new machine learning strategy …

The traditional landscape of vehicle lifecycle management systems has several issues,
including widespread fraud, opaque processes, and limited accessibility. As a result, there is …

In this article, we propose a heterogeneous federated learning for classification (HFLC)
model, which divides features into sensitive and nonsensitive for the privacy concerns. The …

Smart grids have become an emerging topic due to net-zero emissions and the rapid
development of artificial intelligence (AI) technology focused on achieving targeted energy …

(English) The rapid advancement of digitalization, combined with the integration of
renewable generation and the development of information and communication technologies …

The field of computational science [1] emerged at the crossroads of computer science,
information technologies, and mathematical modeling. As a vast interdisciplinary domain, it …

Predictive maintenance plays a crucial role across diverse domains, ensuring operational
efficiency and reliability. However, the increasing need for securing predictive maintenance …

Detection and prediction of failures in Automated Guided Vehicles (AGV) are essential for
the uninterrupted operation of production plants. Anomaly detection is usually achieved by …