Federated Learning (FL) suffers from severe performance degradation due to the data
heterogeneity among clients. Some existing work suggests that the fundamental reason is …

Personalized Federated Graph Learning (pFGL) facilitates the decentralized training of
Graph Neural Networks (GNNs) without compromising privacy while accommodating …

Along with the increasing popularity of deep learning (DL) techniques, more and more
Artificial Intelligence of Things (AIoT) systems are adopting federated learning (FL) to enable …

Federated learning (FL) as a promising distributed machine learning paradigm has been
widely adopted in Artificial Intelligence of Things (AIoT) applications. However, the efficiency …

Federated Learning (FL) has been widely adopted to process the enormous data in the
application scenarios like Edge Computing (EC). However, the commonly-used …

Federated learning (FL) enables collaborative learning across multiple clients. In most FL
work, all clients train a single learning task. However, the recent proliferation of FL …

Due to its advantages in resource constraint scenarios, Split Federated Learning (SFL) is
promising in AIoT systems. However, due to data heterogeneity and stragglers, SFL suffers …

Over the past several years, various federated learning (FL) methodologies have been
developed to improve model accuracy, a primary performance metric in machine learning …

Although Split Federated Learning (SFL) is good at enabling knowledge sharing among
resource-constrained clients, it suffers from the problem of low training accuracy due to the …

With advancements in AI infrastructure and Trusted Execution Environment (TEE)
technology, Federated Learning as a Service (FLaaS) through JointCloud Computing (JCC) …