There is an increasing interest in a fast-growing machine learning technique called
Federated Learning (FL), in which the model training is distributed over mobile user …

Wireless federated learning (FL) is a collaborative machine learning (ML) framework in
which wireless client-devices independently train their ML models and send the locally …

Traditional Federated Learning (FL) is a promising paradigm that enables massive edge
clients to collaboratively train deep neural network (DNN) models without exposing raw data …

Federated learning (FL) enables distributed mobile devices to collaboratively learn a shared
model without exposing their raw data. However, heterogeneous devices usually have …

Federated edge learning is a promising technology to deploy intelligence at the edge of
wireless networks in a privacy-preserving manner. Under such a setting, multiple clients …

Federated learning (FL), an emerging distributed machine learning (ML) technique, allows
massive embedded devices and a server to work together for training a global ML model …

Federated learning is a promising technique to implement network intelligence for the sixth
generation (6G) communication systems. However, the collected data in wireless networks …

Over the past few years, Federated Learning (FL) has become a popular distributed
machine learning paradigm. FL involves a group of clients with decentralized data who …

Federated learning has emerged as a popular technique for distributing machine learning
(ML) model training across the wireless edge. In this paper, we propose two timescale …

Federated learning (FL), as a nascent distributed learning framework, trains a machine
learning model in a collaborative manner. Synchronous model aggregation is widely …