Unleashing the power of edge-cloud generative ai in mobile networks: A survey of aigc services

M Xu, H Du, D Niyato, J Kang, Z Xiong… - … Surveys & Tutorials, 2024 - ieeexplore.ieee.org
Artificial Intelligence-Generated Content (AIGC) is an automated method for generating,
manipulating, and modifying valuable and diverse data using AI algorithms creatively. This …

Federated learning for internet of things: A comprehensive survey

DC Nguyen, M Ding, PN Pathirana… - … Surveys & Tutorials, 2021 - ieeexplore.ieee.org
The Internet of Things (IoT) is penetrating many facets of our daily life with the proliferation of
intelligent services and applications empowered by artificial intelligence (AI). Traditionally …

Edge learning for B5G networks with distributed signal processing: Semantic communication, edge computing, and wireless sensing

W Xu, Z Yang, DWK Ng, M Levorato… - IEEE journal of …, 2023 - ieeexplore.ieee.org
To process and transfer large amounts of data in emerging wireless services, it has become
increasingly appealing to exploit distributed data communication and learning. Specifically …

Beyond transmitting bits: Context, semantics, and task-oriented communications

D Gündüz, Z Qin, IE Aguerri, HS Dhillon… - IEEE Journal on …, 2022 - ieeexplore.ieee.org
Communication systems to date primarily aim at reliably communicating bit sequences.
Such an approach provides efficient engineering designs that are agnostic to the meanings …

Edge artificial intelligence for 6G: Vision, enabling technologies, and applications

KB Letaief, Y Shi, J Lu, J Lu - IEEE Journal on Selected Areas …, 2021 - ieeexplore.ieee.org
The thriving of artificial intelligence (AI) applications is driving the further evolution of
wireless networks. It has been envisioned that 6G will be transformative and will …

A survey on federated learning for resource-constrained IoT devices

A Imteaj, U Thakker, S Wang, J Li… - IEEE Internet of Things …, 2021 - ieeexplore.ieee.org
Federated learning (FL) is a distributed machine learning strategy that generates a global
model by learning from multiple decentralized edge clients. FL enables on-device training …

Evolution of NOMA toward next generation multiple access (NGMA) for 6G

Y Liu, S Zhang, X Mu, Z Ding, R Schober… - IEEE Journal on …, 2022 - ieeexplore.ieee.org
Due to the explosive growth in the number of wireless devices and diverse wireless
services, such as virtual/augmented reality and Internet-of-Everything, next generation …

Distributed learning in wireless networks: Recent progress and future challenges

M Chen, D Gündüz, K Huang, W Saad… - IEEE Journal on …, 2021 - ieeexplore.ieee.org
The next-generation of wireless networks will enable many machine learning (ML) tools and
applications to efficiently analyze various types of data collected by edge devices for …

Federated machine learning: Survey, multi-level classification, desirable criteria and future directions in communication and networking systems

OA Wahab, A Mourad, H Otrok… - … Surveys & Tutorials, 2021 - ieeexplore.ieee.org
The communication and networking field is hungry for machine learning decision-making
solutions to replace the traditional model-driven approaches that proved to be not rich …

Federated learning for internet of things: Recent advances, taxonomy, and open challenges

LU Khan, W Saad, Z Han, E Hossain… - … Surveys & Tutorials, 2021 - ieeexplore.ieee.org
The Internet of Things (IoT) will be ripe for the deployment of novel machine learning
algorithm for both network and application management. However, given the presence of …