In recent years, Federated Learning (FL) has gained relevance in training collaborative
models without sharing sensitive data. Since its birth, Centralized FL (CFL) has been the …

Federated learning (FL) offers an effective learning architecture to protect data privacy in a
distributed manner. However, the inevitable network asynchrony, overdependence on a …

Abstract The rise of Decentralized Federated Learning (DFL) has enabled the training of
machine learning models across federated participants, fostering decentralized model …

Industry 4.0 has brought numerous advantages, such as increasing productivity through
automation. However, it also presents major cybersecurity issues such as cyberattacks …

The rapid integration of Federated Learning (FL) into networking encompasses various
aspects such as network management, quality of service, and cybersecurity while preserving …

In the field of heterogeneous federated learning (FL), the key challenge is to efficiently and
collaboratively train models across multiple clients with different data distributions, model …

The pressing need for sustainable development solutions necessitates innovative data-
driven tools. Machine learning (ML) offers significant potential, but faces challenges in …

There is significant interest in edge computing (EC) for computational social systems to
process and store data at the edge of the network. One of the key applications of EC is to …

Abstract The Federated Learning Secure Model Repository presents a novel paradigm to
ensure the trustworthiness of machine learning models generated through federated …

Federated learning (FL) enables collaborative and privacy-preserving training of machine
learning (ML) models on federated data. However, the barriers to using FL are still high …