In this paper, we propose a semi-federated learning (SemiFL) framework that allows
computing-limited clients to collaboratively train a shared model with resource-abundant …

We study over-the-air model aggregation in federated edge learning (FEEL) systems, where
channel state information at the transmitters (CSIT) is assumed to be unavailable. We …

In this paper, we propose a semi-federated learning (SemiFL) framework to ameliorate the
performance of conventional federated learning. The base station and devices are …

Federated edge learning (FEEL) as an emerging distributed learning paradigm can
effectively resolve the resource constraints and privacy issues in the Internet of Things (IoT) …

Over-the-air computation (OAC) is a promising technique to realize fast model aggregation
in the uplink of federated edge learning (FEEL). OAC, however, hinges on accurate channel …

Edge intelligence refers to utilize a large number of distributed data and computing
resources to learn and inference directly at network edge. Federated edge learning (FEEL) …

To achieve an efficient federated edge learning (FEEL) system, the scheme of device
scheduling and resource allocation should jointly perceive the device availability, wireless …

Over-the-air computation (AirComp) is a promising technology converging communication
and computation over wireless networks, which can be particularly effective in model …

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 …

Over-the-air federated learning (OTA-FL) exploits the inherent superposition property of
wireless channels to integrate the communication and model aggregation. Though a …