With billions of IoT devices producing vast data globally, privacy and efficiency challenges
arise in AI applications. Federated learning (FL) has been widely adopted to train deep …

Edge federated learning (FL) is an emerging paradigm that trains a global parametric model
from distributed datasets based on wireless communications. This paper proposes a unit …

The newly emerging federated learning (FL) framework offers a new way to train machine
learning models in a privacy-preserving manner. However, traditional FL algorithms are …

Over-the-air federated learning (OTA-FL) integrates communication and model aggregation
by exploiting the innate superposition property of wireless channels. The approach renders …

Federated learning (FL) has prevailed as an efficient and privacy-preserved scheme for
distributed learning. In this work, we mainly focus on the optimization of computation and …

The provision of communication services via portable and mobile devices, such as aerial
base stations, is a crucial concept to be realized in 5G/6G networks. Conventionally …

Training high-quality machine learning models on distributed systems is a critical issue to
achieve edge intelligence in wireless communications. Conventional data-driven machine …

As a decentralized training paradigm, Federated learning (FL) promises data privacy by
exchanging model parameters instead of raw local data. However, it is still impeded by the …

Federated learning (FL) embraces the concepts of targeted data gathering and training, and
it can reduce many of the systemic privacy costs and hazards associated with traditional …

Federated learning (FL) is an emerging distributed training scheme where edge devices
collaboratively train a model by uploading model updates instead of private data. To …