The advances of both computation and communication technologies facilitate the
exploitation of massive data generated by mobile devices. It is attractive to leverage these …

This letter investigates an hierarchical federated learning (HFL) framework with partial
aggregation in a resource-constrained multi-tier wireless network. Due to device …

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 …

While a practical wireless network has many tiers where end users do not directly
communicate with the central server, the users' devices have limited computation and …

The conventional federated learning (FL) framework usually assumes synchronous
reception and fusion of all the local models at the central aggregator and synchronous …

In this article, we study the latency minimization problem for a wireless federated learning
(FL) system with heterogeneous computation capability, where different edge devices …

This article proposes using communication pipelining to enhance the convergence speed of
federated learning in mobile edge computing applications. Due to limited wireless …

Federated learning (FL) has recently become one of the hottest focuses in wireless edge
networks with the ever-increasing computing capability of user equipment (UE). In FL, UEs …

Federated Learning (FL) is a collaborative machine learning (ML) framework that combines
on-device training and server-based aggregation to train a common ML model among …

Federated learning provides a promising paradigm to enable network edge intelligence in
the future sixth generation (6G) systems. However, due to the high dynamics of wireless …