As a popular distributed machine learning based on stochastic gradient decent (SGD),
federated learning enables edge devices to compute stochastic gradients and then upload …

In this letter, we introduce over-the-air computation into the communication design of
federated multi-task learning (FMTL), and propose an over-the-air federated multi-task …

Federated learning (FL) is an emerging distributed learning paradigm, which aims to train
machine learning models on geo-decentralized edge devices while keeping the training …

Federated Learning (FL) has been proposed as an appealing approach to handle data
privacy issue of mobile devices compared to conventional machine learning at the remote …

Personalized federated learning enables every edge device or group of edge devices within
the distributed network to learn a device-or cluster-specific model tailored to their local …

Artificial intelligence (AI) is expected as a revo-lutionary technology to be widely used in
Internet-of-Things (IoT) networks for computationally intensive tasks. However, the traditional …

Federated learning (FL) over wireless communication channels, specifically, over-the-air
(OTA) model aggregation framework is considered. In OTA wireless setups, the adverse …

Over-the-air federated edge learning (Air-FEEL) is a communication-efficient framework for
distributed machine learning using training data distributed at edge devices. This framework …

Federated Learning (FL) enables a large number of resource-limited devices to train a
model collaboratively without data sharing. However, many works focus on model …

We consider a broadband over-the-air computation empowered model aggregation
approach for wireless federated learning (FL) systems and propose to leverage an …