Federated learning (FL) has emerged as a popular paradigm for distributed machine
learning among vast clients. Unfortunately, resource-constrained clients often fail to …

Federated Learning (FL) is a distributed model training framework that allows multiple
clients to collaborate on training a global model without disclosing their local data in edge …

Federated learning (FL) has been proposed as a promising distributed learning paradigm to
realize edge artificial intelligence (AI) without revealing the raw data. Nevertheless, it would …

Federated Learning (FL) has achieved state-of-the-art performance in training a global
model in a decentralized and privacy-preserving manner. Many recent works have …

In this article, we propose Hierarchical Federated Learning with Momentum Acceleration
(HierMo), a three-tier worker-edge-cloud federated learning algorithm that applies …

Hierarchical federated learning has been studied as a more practical approach to federated
learning in terms of scalability, robustness, and privacy protection, particularly in edge …

Federated Learning (FL), a promising privacy-preserving distributed learning paradigm, has
been extensively applied in urban environmental prediction tasks of Mobile Edge …

Federated learning (FL) has been emerging as a new distributed machine learning
paradigm recently. Although FL can protect the data privacy of participants by keeping their …

In edge computing (EC), federated learning (FL) enables numerous distributed devices (or
workers) to collaboratively train AI models without exposing their local data. Most works of …

Federated learning has gained popularity as a means of training models distributed across
the wireless edge. The paper introduces delay-aware hierarchical federated learning (DFL) …