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 …

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 …

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 …

Federated learning on non-IID data: A survey

H Zhu, J Xu, S Liu, Y Jin - Neurocomputing, 2021 - Elsevier
Federated learning is an emerging distributed machine learning framework for privacy
preservation. However, models trained in federated learning usually have worse …

Digital-twin-enabled 6G: Vision, architectural trends, and future directions

LU Khan, W Saad, D Niyato, Z Han… - IEEE Communications …, 2022 - ieeexplore.ieee.org
Internet of Everything (IoE) applications such as haptics, human-computer interaction, and
extended reality, using the sixth-generation (6G) of wireless systems have diverse …

A review of convolutional neural network architectures and their optimizations

S Cong, Y Zhou - Artificial Intelligence Review, 2023 - Springer
The research advances concerning the typical architectures of convolutional neural
networks (CNNs) as well as their optimizations are analyzed and elaborated in detail in this …

{BatchCrypt}: Efficient homomorphic encryption for {Cross-Silo} federated learning

C Zhang, S Li, J Xia, W Wang, F Yan, Y Liu - 2020 USENIX annual …, 2020 - usenix.org
Cross-silo federated learning (FL) enables organizations (eg, financial, or medical) to
collaboratively train a machine learning model by aggregating local gradient updates from …

Model compression and hardware acceleration for neural networks: A comprehensive survey

L Deng, G Li, S Han, L Shi, Y Xie - Proceedings of the IEEE, 2020 - ieeexplore.ieee.org
Domain-specific hardware is becoming a promising topic in the backdrop of improvement
slow down for general-purpose processors due to the foreseeable end of Moore's Law …

Federated deep learning for cyber security in the internet of things: Concepts, applications, and experimental analysis

MA Ferrag, O Friha, L Maglaras, H Janicke… - IEEE Access, 2021 - ieeexplore.ieee.org
In this article, we present a comprehensive study with an experimental analysis of federated
deep learning approaches for cyber security in the Internet of Things (IoT) applications …

Robust and communication-efficient federated learning from non-iid data

F Sattler, S Wiedemann, KR Müller… - IEEE transactions on …, 2019 - ieeexplore.ieee.org
Federated learning allows multiple parties to jointly train a deep learning model on their
combined data, without any of the participants having to reveal their local data to a …