Federated learning (FL) introduces new perspectives in machine learning (ML) by enabling
model training across decentralized devices. The research on data security and privacy in …

Recently, the application of finger vein recognition has become popular. Studies have
shown finger vein presentation attacks increasingly threaten these recognition devices. As a …

Federated Learning (FL) shows promise in preserving privacy and enabling collaborative
learning. However, most current solutions focus on private data collected from a single …

Federated learning (FL) has recently garnered attention as a data-decentralized training
framework that enables the learning of deep models from locally distributed samples while …

Federated learning ensures the privacy of clients by conducting distributed training on
individual client devices and sharing only the model weights with a central server. However …

Artificial intelligence has been integrated into nearly every aspect of daily life, powering
applications from object detection with computer vision to large language models for writing …

In multi‐access edge computing (MEC), computational task offloading of mobile terminals
(MT) is expected to provide the green applications with the restriction of energy consumption …