Knowledge sharing and model personalization are essential components to tackle the non-
IID challenge in federated learning (FL). Most existing FL methods focus on two extremes: 1) …

We carefully evaluate a number of algorithms for learning in a federated environment, and
test their utility for a variety of image classification tasks. We consider many issues that have …

Heterogeneous federated learning without assuming any structure is challenging due to the
conflicts among non-identical data distributions of clients. In practice, clients often comprise …

Ensuring data privacy is an essential objective competing with the ever-rising capabilities of
machine learning approaches fueled by vast amounts of centralized data. Federated …

The cyber-threat landscape has evolved tremendously in recent years, with new threat
variants emerging daily, and large-scale coordinated campaigns becoming more prevalent …

Federated learning (FL) has enabled training machine learning models exploiting the data
of multiple agents without compromising privacy. However, FL is known to be vulnerable to …

With billions of IoT devices producing vast data globally, privacy and efficiency challenges
arise in AI applications. Federated learning (FL) has been widely adopted to train deep …

The cyber threat landscape has evolved tremendously in recent years, with new threat
variants emerging daily, and large-scale coordinated campaigns becoming more prevalent …

Federated learning (FL) has gained great traction in recent years. It can provide a privacy-
preserving mechanism to train machine learning models on hidden data. However, most of …

Autonomous computing is the key concept for service orchestration in next-generation cloud
computing environments. Virtualization supplements it by adding an abstraction layer to the …