Federated Learning (FL) is a secure distributed learning paradigm, which enables
potentially a large number of devices to collaboratively train a global model based on their …

Recently, federated learning (FL) has gained momentum because of its capability in
preserving data privacy. To conduct model training by FL, multiple clients exchange model …

Distributed training includes two important operations: gradient transmission and gradient
aggregation, which will consume massive bandwidth and computing resources. To achieve …

In Federated Learning (FL) paradigm, a parameter server (PS) concurrently communicates
with distributed participating clients for model collection, update aggregation, and model …

Federated learning (FL) in large-scale wireless networks is implemented in a hierarchical
way by introducing edge servers as relays between the cloud server and devices, where …

Federated learning is a new distributed machine learning model training method, which can
protect user privacy while enabling a large number of edge devices to train a shared model …

Federated Learning (FL), as an emerging paradigm in distributed machine learning, has
received extensive research attention. However, few works consider the impact of device …

In Federated Learning (FL), to improve the training efficiency, we don't need to let all of the
clients join in the training process. Instead, we can select some specific clients to join in the …

The proliferation of the Internet of Things (IoT) and the advancements in machine learning
(ML) have facilitated ubiquitous sensing and computing capabilities, enabling the …

Federated Learning (FL) enables distributed clients to collaboratively train models without
exposing their private data. However, it is difficult to implement efficient FL due to limited …