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 …

Abstract Decentralized Federated Learning (DFL) is a prevalent approach to efficiently train
deep learning models and preserve privacy by sharing model gradients instead of the local …

The unprecedented technological advancements in Artificial Intelligence (AI) and the
Internet of Things (IoT) have given rise to ecosystems of intelligent, interconnected devices …

In response to the susceptibility of federated learning, which is based on a distributed
training structure, to byzantine poisoning attacks from malicious clients, resulting in issues …

Federated learning (FL) is a type of distributed learning that can perform model training
without exposing end users' data from end-user devices to increase security. Although it is …

With the advent of data-driven and Artificial Intelligence solutions, data providers such as
Internet of Things (IoT)-enabled devices and sensors have become essential for intelligent …

Federated learning has recently emerged as a decentralized approach to learn a high-
performance model without access to user data. Despite its effectiveness, federated learning …

Federated learning is a distributed machine learning approach in which end-user devices
update the learning model by training on their local data, rather than on a central server …

Artificial intelligence training in a single center requires sharing personal data with third
parties. In methods such as federated learning, where training is provided without collecting …

Conventional AI-based service flow remains a challenge for IoT-enabled devices since data
collected by local clients are transferred to a centralized server, which contains a global …