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 …

A state-of-the-art survey on solving non-iid data in federated learning

X Ma, J Zhu, Z Lin, S Chen, Y Qin - Future Generation Computer Systems, 2022 - Elsevier
Federated Learning (FL) proposed in recent years has received significant attention from
researchers in that it can enable multiple clients to cooperatively train global models without …

Fine-tuning global model via data-free knowledge distillation for non-iid federated learning

L Zhang, L Shen, L Ding, D Tao… - Proceedings of the …, 2022 - openaccess.thecvf.com
Federated Learning (FL) is an emerging distributed learning paradigm under privacy
constraint. Data heterogeneity is one of the main challenges in FL, which results in slow …

Combining federated learning and edge computing toward ubiquitous intelligence in 6G network: Challenges, recent advances, and future directions

Q Duan, J Huang, S Hu, R Deng… - … Surveys & Tutorials, 2023 - ieeexplore.ieee.org
Full leverage of the huge volume of data generated on a large number of user devices for
providing intelligent services in the 6G network calls for Ubiquitous Intelligence (UI). A key to …

Federated learning on non-IID data: A survey

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

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 …

Survey on 6G frontiers: Trends, applications, requirements, technologies and future research

C De Alwis, A Kalla, QV Pham, P Kumar… - IEEE Open Journal …, 2021 - ieeexplore.ieee.org
Emerging applications such as Internet of Everything, Holographic Telepresence,
collaborative robots, and space and deep-sea tourism are already highlighting the …

Federated learning meets blockchain in edge computing: Opportunities and challenges

DC Nguyen, M Ding, QV Pham… - IEEE Internet of …, 2021 - ieeexplore.ieee.org
Mobile-edge computing (MEC) has been envisioned as a promising paradigm to handle the
massive volume of data generated from ubiquitous mobile devices for enabling intelligent …

FELIDS: Federated learning-based intrusion detection system for agricultural Internet of Things

O Friha, MA Ferrag, L Shu, L Maglaras… - Journal of Parallel and …, 2022 - Elsevier
In this paper, we propose a federated learning-based intrusion detection system, named
FELIDS, for securing agricultural-IoT infrastructures. Specifically, the FELIDS system protects …

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 …