Recently, there has been a surge in the development of advanced intelligent generative
content (AIGC), especially large language models (LLMs). However, for many downstream …

Federated Learning enables data owners to train an artificial intelligence model
collaboratively while keeping all the training data locally, reducing the possibility of personal …

Federated Learning (FL) has drawn much attention for distributed system over the Internet of
Things (IoT), since it enables collaborative machine learning on heterogeneous devices …

Federated learning (FL) is a powerful technology that enables collaborative training of
machine learning models without sharing private data among clients. The fundamental …

The integration of the Internet of Things (IoT) and modern Artificial Intelligence (AI) has given
rise to a new paradigm known as the Artificial Intelligence of Things (AIoT). In this survey, we …

Federated Learning (FL) has emerged as a powerful approach that enables collaborative
distributed model training without the need for data sharing. However, FL grapples with …

Local stochastic gradient descent (Local-SGD), also referred to as federated averaging, is
an approach to distributed optimization where each device performs more than one SGD …

Federated Learning (FL) achieves great popularity in the Internet of Things (IoT) as a
powerful interface to offer intelligent services to customers while maintaining data privacy …

Federated Learning (FL) has emerged as a prominent distributed machine learning
framework that enables geographically discrete clients to train a global model …

Despite extensive research into data heterogeneity in federated learning (FL), system
heterogeneity remains a significant yet often overlooked challenge. Traditional FL …