Federated learning (FL) has been recognized as a viable distributed learning paradigm
which trains a machine learning model collaboratively with massive mobile devices in the …

Federated learning (FL) is a powerful machine learning paradigm which leverages the data
as well as the computational resources of clients, while protecting clients' data privacy …

Federated learning (FL) has been recognized as a viable distributed learning paradigm
which trains a machine learning model collaboratively with massive mobile devices in the …

Federated learning (FL) is a distributed machine learning technique to address the data
privacy issue. Participant selection is critical to determine the latency of the training process …

Federated Learning (FL) is a distributed machine learning technique designed to utilize the
distributed datasets collected by our mobile and internet-of-things devices. As such, it is …

Federated learning (FL) is a framework of large-scale distributed learning with user privacy
protection through local training and global aggregation. However, FL may suffer from …

With the fast development of modern networking technologies, the transmission rate and
reliability of wireless networks have been greatly improved. Meanwhile, the fast-developing …

The sixth-generation (6G) wireless communication is expected to support ubiquitous artificial
intelligence (AI) applications from the network core to the end devices. The computational …

Recently, the implementation of federated learning (FL) in wireless networks becomes a
hotspot due to its flexible collaborative learning methods and privacy-preserving benefits …

Because of its privacy-preserving capability, federated learning (FL) has attracted significant
attention from both academia and industry. However, when being implemented over …