Federated Learning (FL) has recently arisen as a revolutionary approach to collaborative
training Machine Learning models. According to this novel framework, multiple participants …

Federated Learning (FL) has emerged as a significant advancement in the field of Artificial
Intelligence (AI), enabling collaborative model training across distributed devices while …

Federated learning, leveraging distributed data from multiple nodes to train a common
model, allows for the use of more data to improve the model while also protecting the privacy …

Data is readily available with the growing number of smart and IoT devices. However,
application-specific data is available in small chunks and distributed across demographics …

The heterogeneous connections in metaverse environments pose vulnerabilities to cyber-
attacks. To prevent and mitigate malicious network activities in a distributed metaverse …

Federated Learning (FL) emerged as a significant advancement in the field of Artificial
Intelligence (AI), enabling collaborative model training across distributed devices while …

Federated learning (FL) enables the collaborative training of machine learning models
without sharing training data. Traditional FL heavily relies on a trusted centralized server …

Federated Learning (FL) is a promising solution for training using data collected from
heterogeneous sources (eg, mobile devices) while avoiding the transmission of large …

The sixth-generation (6G) wireless networks are envisioned to build a data-driven digital
world with widespread Artificial Intelligence (AI). Federated learning (FL) is a distributed AI …

With the widespread deployment of multi-sensors on vehicles, a significant amount of data is
generated that exhibits the characteristics of massive volume, wide variety, and privacy …