Distributed machine learning (DML) techniques, such as federated learning, partitioned
learning, and distributed reinforcement learning, have been increasingly applied to wireless …

The success of machine learning (ML) techniques in the formerly difficult areas of data
analysis and pattern extraction has led to their widespread incorporation into various …

Graph neural networks (GNNs) have emerged due to their success at modeling graph data.
Yet, it is challenging for GNNs to efficiently scale to large graphs. Thus, distributed GNNs …

In collaborative machine learning (CML), multiple agents pool their resources (eg, data)
together for a common learning task. In realistic CML settings where the agents are self …

Federated learning (FL) is a machine learning model that preserves data privacy in the
training process. Specifically, FL brings the model directly to the user equipments (UEs) for …

Graph Neural Networks (GNNs) are the first choice methods for graph machine learning
problems thanks to their ability to learn state-of-the-art level representations from graph …

This paper presents new asynchronous methods to the Federated Learning (FL), one of the
next-generation paradigms for Artificial Intelligence (AI) systems. We consider the two-fold …

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

Abstract In cross-device Federated Learning (FL) environments, scaling synchronous FL
methods is challenging as stragglers hinder the training process. Moreover, the availability …

Motivated by the ever-increasing demands for massive data processing and intelligent data
analysis at the network edge, federated learning (FL), a distributed architecture for machine …