When implementing hierarchical federated learning over wireless networks, scalability
assurance and the ability to handle both interference and device data heterogeneity are …

When implementing hierarchical federated learning over wireless networks, scalability
assurance and the ability to handle both interference and device data heterogeneity are …

Federated Learning (FL) algorithms commonly sample a random subset of clients to address
the straggler issue and improve communication efficiency. While recent works have …

In this work, hierarchical federated learning (HFL) over wireless multi-cell networks is
proposed for large-scale model training while preserving data privacy. However, the …

In this paper, we consider communication-efficient over-the-air federated learning (FL),
where multiple edge devices with non-independent and identically distributed datasets …

Despite the recent advancements achieved by federated learning (FL), its real-world
deployment is significantly impeded by the heterogeneous learning environment …

With the rapid proliferation of smart mobile devices, federated learning (FL) has been widely
considered for application in wireless networks for distributed model training. However, data …

Federated learning (FL) is a promising technique that enables many edge devices to train a
machine learning model collaboratively in wireless networks. By exploiting the superposition …

Federated learning (FL) is promising in enabling large-scale model training by massive
devices without exposing their local datasets. However, due to limited wireless resources …

Over-the-air federated learning (OTA-FL) has emerged as an efficient mechanism that
exploits the superposition property of the wireless medium and performs model aggregation …