In the realm of federated learning (FL), the conventional dual-layered architecture,
comprising a central parameter server and peripheral devices, often encounters challenges …

Abstract The rise of Federated Learning (FL) is bringing machine learning to edge
computing by utilizing data scattered across edge devices. However, the heterogeneity of …

Federated learning (FL) enables multiple devices to collaboratively train a shared model
while preserving data privacy. However, despite its emerging applications in many areas …

Federated learning (FL) is a collaborative machine learning paradigm where network-edge
clients train a global model under the orchestration of a central server. Unlike traditional …

Federated learning (FL) is widely regarded as highly promising because it enables the
collaborative training of high-performance machine learning models among a large number …

Federated learning (FL), as an emerging artificial intelligence (AI) approach, enables
decentralized model training across multiple devices without exposing their local training …

In Federated Learning (FL), the size of local models matters. On the one hand, it is logical to
use large-capacity neural networks in pursuit of high performance. On the other hand, deep …

Federated Learning (FL) enables training Artificial Intelligence (AI) models over end devices
without compromising their privacy. As computing tasks are increasingly performed by a …

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 …

In the rapidly advancing landscape of machine learning, Federated Learning (FL) stands as
a transformative paradigm, preserving data privacy and overcoming challenges in training …