Machine learning (ML) is a promising enabler for the fifth-generation (5G) communication systems and beyond. By imbuing intelligence into the network edge, edge nodes can …
With its privacy-preserving and decentralized features, distributed learning plays an irreplaceable role in the era of wireless networks with a plethora of smart terminals, an …
Distributed machine learning (DML) techniques, such as federated learning, partitioned learning, and distributed reinforcement learning, have been increasingly applied to wireless …
Z Qin, GY Li, H Ye - IEEE Wireless Communications, 2021 - ieeexplore.ieee.org
Federated learning becomes increasingly attractive in the areas of wireless communications and machine learning due to its powerful learning ability and potential applications. In …
Distributed learning is envisioned as the bedrock of next-generation intelligent networks, where intelligent agents, such as mobile devices, robots, and sensors, exchange information …
Machine learning (ML) tasks are becoming ubiquitous in today's network applications. Federated learning has emerged recently as a technique for training ML models at the …
Z Meng, H Xu, M Chen, Y Xu, Y Zhao… - IEEE INFOCOM 2021 …, 2021 - ieeexplore.ieee.org
Data generated at the network edge can be processed locally by leveraging the paradigm of edge computing. To fully utilize the widely distributed data, we concentrate on a wireless …
Z Lin, G Zhu, Y Deng, X Chen, Y Gao… - IEEE Transactions …, 2024 - ieeexplore.ieee.org
The increasingly deeper neural networks hinder the democratization of privacy-enhancing distributed learning, such as federated learning (FL), to resource-constrained devices. To …
J Park, S Samarakoon, M Bennis… - Proceedings of the …, 2019 - ieeexplore.ieee.org
Fueled by the availability of more data and computing power, recent breakthroughs in cloud- based machine learning (ML) have transformed every aspect of our lives from face …