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 …

Distributed artificial intelligence empowered by end-edge-cloud computing: A survey

S Duan, D Wang, J Ren, F Lyu, Y Zhang… - … Surveys & Tutorials, 2022 - ieeexplore.ieee.org
As the computing paradigm shifts from cloud computing to end-edge-cloud computing, it
also supports artificial intelligence evolving from a centralized manner to a distributed one …

Green edge AI: A contemporary survey

Y Mao, X Yu, K Huang, YJA Zhang… - Proceedings of the …, 2024 - ieeexplore.ieee.org
Artificial intelligence (AI) technologies have emerged as pivotal enablers across a multitude
of industries, including consumer electronics, healthcare, and manufacturing, largely due to …

FRUIT: A blockchain-based efficient and privacy-preserving quality-aware incentive scheme

C Zhang, M Zhao, L Zhu, W Zhang… - IEEE Journal on …, 2022 - ieeexplore.ieee.org
Incentive plays an important role in knowledge discovery, as it impels users to provide high-
quality knowledge. To promise incentive schemes with transparency, blockchain technology …

Efficient federated learning with spike neural networks for traffic sign recognition

K Xie, Z Zhang, B Li, J Kang, D Niyato… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
With the gradual popularization of self-driving, it is becoming increasingly important for
vehicles to smartly make the right driving decisions and autonomously obey traffic rules by …

Communication-efficient and cross-chain empowered federated learning for artificial intelligence of things

J Kang, X Li, J Nie, Y Liu, M Xu, Z Xiong… - … on Network Science …, 2022 - ieeexplore.ieee.org
Conventional machine learning approaches aggregate all training data in a central server,
which causes massive communication overhead of data transmission and is also vulnerable …

Burst-aware time-triggered flow scheduling with enhanced multi-CQF in time-sensitive networks

D Yang, Z Cheng, W Zhang, H Zhang… - IEEE/ACM Transactions …, 2023 - ieeexplore.ieee.org
Deterministic transmission guarantee in time-sensitive networks (TSN) relies on queue
models (such as CQF, TAS, ATS) and resource scheduling algorithms. Thanks to its ease of …

Decentralized federated learning: A survey and perspective

L Yuan, Z Wang, L Sun, SY Philip… - IEEE Internet of Things …, 2024 - ieeexplore.ieee.org
Federated learning (FL) has been gaining attention for its ability to share knowledge while
maintaining user data, protecting privacy, increasing learning efficiency, and reducing …

A reinforcement learning-driven brain storm optimisation algorithm for multi-objective energy-efficient distributed assembly no-wait flow shop scheduling problem

F Zhao, X Hu, L Wang, T Xu, N Zhu… - International Journal of …, 2023 - Taylor & Francis
A reinforcement learning-driven brain storm optimisation idea (RLBSO) is proposed in this
paper to solve multi-objective energy-efficient distributed assembly no-wait flow shop …

Internet of intelligence: A survey on the enabling technologies, applications, and challenges

Q Tang, FR Yu, R Xie, A Boukerche… - … Surveys & Tutorials, 2022 - ieeexplore.ieee.org
The Internet of Intelligence is conceived as an emerging networking paradigm, which will
make intelligence as easy to obtain as information. This paper provides an overview of the …