Federated learning (FL) is a distributed machine learning (ML) approach that enables
models to be trained on client devices while ensuring the privacy of user data. Model …

In recent years, the notion of “the right to be forgotten”(RTBF) has become a crucial aspect of
data privacy for digital trust and AI safety, requiring the provision of mechanisms that support …

The individual and integration use of the Internet of Things (IoT), Information-Centric
Networking (ICN), and Federated Learning (FL) have recently been used in several network …

Federated learning (FL) offers distributed machine learning on edge devices. However, the
FL model raises privacy concerns. Various techniques, such as homomorphic encryption …

The success of machine learning (ML) techniques in the formerly difficult areas of data
analysis and pattern extraction has led to their widespread incorporation into various …

The deployment of the fifth-generation (5G) wireless networks in Internet of Everything (IoE)
applications and future networks (eg, sixth-generation (6G) networks) has raised a number …

Machine Unlearning (MU) has recently gained considerable attention due to its potential to
achieve Safe AI by removing the influence of specific data from trained Machine Learning …

Federated learning (FL) is a collaborative artificial intelligence (AI) approach that enables
distributed training of AI models without data sharing, thereby promoting privacy by design …

In a vertical federated learning (VFL) system consisting of a central server and many
distributed clients, the training data are vertically partitioned such that different features are …

Federated learning has gained extensive interest in recent years owing to its ability to
update model parameters without obtaining raw data from users, which makes it a viable …