The Internet of Things (IoT) ecosystem connects physical devices to the internet, offering
significant advantages in agility, responsiveness, and potential environmental benefits. The …

As the computing paradigm shifts from cloud computing to end-edge-cloud computing, it
also supports artificial intelligence evolving from a centralized manner to a distributed one …

The prosperity of machine learning has been accompanied by increasing attacks on the
training process. Among them, poisoning attacks have become an emerging threat during …

Federated learning (FL) is a recent development in artificial intelligence, which is typically
based on the concept of decentralized data. As cyberattacks are frequently happening in the …

Abstract Machine learning are vulnerable to the threats. The Intruders can utilize the
malicious nature of the nodes to attack the training dataset to worsen the process and …

Federated learning is a machine learning paradigm that emerges as a solution to the privacy-
preservation demands in artificial intelligence. As machine learning, federated learning is …

Federated learning (FL) has drawn increasing attention owing to its potential use in large-
scale industrial applications. Existing FL works mainly focus on model homogeneous …

Federated learning (FL) becomes popular and has shown great potentials in training large-
scale machine learning (ML) models without exposing the owners' raw data. In FL, the data …

Sixth-generation (6G) cellular systems will have an inherent vulnerability to physical (PHY)-
layer attacks and privacy leakage, due to the large-scale heterogeneous networks with …

Federated learning (FL) provides a high efficient decentralized machine learning framework,
where the training data remains distributed at remote clients in a network. Though FL …