Wireless Federated Learning (FL) is an emerging distributed machine learning paradigm,
particularly gaining momentum in domains with confidential and private data on mobile …

Federated learning (FL) is an efficient and privacy-preserving distributed learning paradigm
that enables massive edge devices to train machine learning models collaboratively …

Federated Learning (FL) serves as a machine learning paradigm where distributed devices
collaboratively train on local data, with their models subsequently aggregated on a central …

Machine Learning (ML) is a critical tool for data-driven classification and regression tasks.
With the increasing availability of computationally powerful wireless edge devices …

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

Federated learning (FL) is a distributed privacy-preserving paradigm of machine learning
that enables efficient and secure model training through the collaboration of multiple clients …

Federated learning is a distributed machine learning paradigm that allows multiple edge
devices to collaboratively train a shared model without exchanging raw data. However, the …

Federated learning (FL) allows model training from local data by edge devices while
preserving data privacy. However, the learning accuracy decreases due to the heterogeneity …

The dilemma between the ever-increasing demands for data processing, and the limited
capabilities of mobile devices in a wireless communication system calls for the appearance …

Federated learning (FL) is a cutting-edge approach to machine learning where multiple
clients (or nodes) collaboratively train a model while keeping their data localized. This …