Data poisoning attack is one of the common attacks that decreases the performance of a
model in edge machine learning. The mechanism used in most of the existing data …

Deep learning (DL) can provide critical infrastructure operators with valuable insights and
predictive capabilities to help them make more informed decisions, improving system's …

Federated learning (FL) has emerged as a promising solution to address the challenges
posed by data silos and the need for global data fusion. It offers a distributed machine …

In the ever-expanding Internet of Things (IoT) domain, the production of data has reached an
unparalleled scale. This massive data is processed to glean invaluable insights …

Federated Learning is a distributed machine learning method that offers inherent
advantages in efficient learning and privacy protection within edge computing scenarios …

The amount of data generated owing to the rapid development of the Smart Internet of
Things is increasing exponentially. Traditional machine learning can no longer meet the …

With the advanced development of Metaverse, various service requests are required to be
processed as soon as possible which contains a series of tasks with topology structure …

The proliferation of edge computing, driven by network applications and wireless devices,
increases the vulnerability of confidential information to security risks. In this environment …

Federated learning (FL) is a distributed approach to train machine learning models without
disclosing private data from participating clients to a central server. Nevertheless, FL training …

Digital forensics plays a crucial role in the justice system, providing digital evidence that can
be used to prosecute and convict criminals. This field is rapidly evolving due to the …