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 …

Federated learning for 6g: Paradigms, taxonomy, recent advances and insights

MB Driss, E Sabir, H Elbiaze, W Saad - arXiv preprint arXiv:2312.04688, 2023 - arxiv.org
Artificial Intelligence (AI) is expected to play an instrumental role in the next generation of
wireless systems, such as sixth-generation (6G) mobile network. However, massive data …

A survey on reinforcement learning-aided caching in heterogeneous mobile edge networks

N Nomikos, S Zoupanos, T Charalambous… - IEEE Access, 2022 - ieeexplore.ieee.org
Mobile networks experience a tremendous increase in data volume and user density due to
the massive number of coexisting users and devices. An efficient technique to alleviate this …

Multi-agent caching strategy for spatial-temporal popularity in iov

P He, L Cao, Y Cui, R Wang… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Motivated by connected vehicles, the internet of vehicles (IoV) has a prosperous
development, thus a variety of IoV applications have emerged, which causes the dramatic …

[HTML][HTML] Federated learning based qos-aware caching decisions in fog-enabled internet of things networks

X Huang, Z Chen, Q Chen, J Zhang - Digital Communications and …, 2023 - Elsevier
Abstract Quality of Service (QoS) in the 6G application scenario is an important issue with
the premise of the massive data transmission. Edge caching based on the fog computing …

MECC: A mobile edge collaborative caching framework empowered by deep reinforcement learning

S Xu, X Liu, S Guo, X Qiu, L Meng - IEEE Network, 2021 - ieeexplore.ieee.org
With the rapid development of smart city and 5G, user demand for Internet services has
increased exponentially. Through collaborative content sharing, the storage limitation of a …

Federated learning-based content popularity prediction in fog radio access networks

Y Jiang, Y Wu, FC Zheng, M Bennis… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
In this paper, the content popularity prediction problem in fog radio access networks (F-
RANs) is investigated. In order to obtain accurate prediction with low complexity, we propose …

Community detection and attention-weighted federated learning based proactive edge caching for D2D-assisted wireless networks

D Li, H Zhang, T Li, H Ding… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
This work investigates proactive edge caching for D2D-assisted wireless networks, where
user equipments (UEs) can be selected as caching nodes to assist content delivery. The …

Optimizing video caching at the edge: A hybrid multi-point process approach

X Zhang, Y Zhou, D Wu, M Hu, X Zheng… - … on Parallel and …, 2022 - ieeexplore.ieee.org
It is always a challenging problem to deliver a huge volume of videos over the Internet. To
meet the high bandwidth and stringent playback demand, one feasible solution is to cache …

An efficient algorithm for data transmission certainty in IIoT sensing network: A priority-based approach

KG Nalbant, S Almutairi, AH Alshehri, H Kemal… - PloS one, 2024 - journals.plos.org
This paper proposes a novel cache replacement technique based on the notion of
combining periodic popularity prediction with size caching. The popularity, size, and time …