Federated Averaging (FedAvg) and its variants are prevalent optimization algorithms
adopted in Federated Learning (FL) as they show good model convergence. However, such …

Federated edge learning is envisioned as the bedrock of enabling intelligence in next-
generation wireless networks, but the limited spectral resources often constrain its …

Federated learning (FL) is a popular privacy-preserving distributed training scheme, where
multiple devices collaborate to train machine learning models by uploading local model …

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 investigate over-the-air model aggregation in a federated edge learning
(FEEL) system. We introduce a Markovian probability model to characterize the intrinsic …

When applying machine learning techniques to the Internet of things, aggregating massive
amount of data seriously reduce the system efficiency. To tackle this challenge, a distributed …

In large-scale federated learning systems, it is common to observe straggler effect from
those clients with slow speed to delay the overall learning. However, in the standard …

Federated learning (FL) is an emerging machine learning paradigm for training models
across multiple edge devices holding local data sets, without explicitly exchanging the data …

Over-the-air gradient aggregation and data-aware scheduling have recently drawn great
attention due to the outstanding performance in improving communication efficiency for …

Federated learning (FL) is an emerging privacy-preserving paradigm that enables multiple
participants collaboratively to train a global model without uploading raw data. Considering …