Federated learning (FL) is a secure distributed machine learning paradigm that addresses
the issue of data silos in building a joint model. Its unique distributed training mode and the …

Federated learning (FL) is a cutting-edge artificial intelligence approach. It is a decentralized
problem-solving technique that allows users to train using massive data. Unprocessed …

Semantic communications could improve the transmission efficiency significantly by
exploring the semantic information. In this paper, we make an effort to recover the …

Decentralized federated learning, inherited from decentralized learning, enables the edge
devices to collaborate on model training in a peer-to-peer manner without the assistance of …

In recent years, artificial intelligence (AI) techniques, especially machine learning (ML), have
been successfully applied in various areas, leading to a widespread belief that AI will …

We consider a semantic communication system for speech signals, named DeepSC-S.
Motivated by the breakthroughs in deep learning (DL), we make an effort to recover the …

One of the crucial issues in federated learning is how to develop efficient optimization
algorithms. Most of the current ones require full device participation and/or impose strong …

Abstract Federated-Learning (FL), a new paradigm in the machine-learning approach,
wherein the clients train the global model collaboratively across various computational …

The machine learning performance usually could be improved by training with massive data.
However, requesters can only select a subset of devices with limited training data to execute …

As data generation increasingly takes place on devices without a wired connection, Machine
Learning (ML) related traffic will be ubiquitous in wireless networks. Many studies have …