Machine learning for large-scale optimization in 6g wireless networks
The sixth generation (6G) wireless systems are envisioned to enable the paradigm shift from
“connected things” to “connected intelligence”, featured by ultra high density, large-scale …
“connected things” to “connected intelligence”, featured by ultra high density, large-scale …
Exploring deep-reinforcement-learning-assisted federated learning for online resource allocation in privacy-preserving edgeiot
Federated learning (FL) has been increasingly considered to preserve data training privacy
from eavesdropping attacks in mobile-edge computing-based Internet of Things (EdgeIoT) …
from eavesdropping attacks in mobile-edge computing-based Internet of Things (EdgeIoT) …
Distributed intelligence in wireless networks
The cloud-based solutions are becoming inefficient due to considerably large time delays,
high power consumption, and security and privacy concerns caused by billions of connected …
high power consumption, and security and privacy concerns caused by billions of connected …
Towards Dynamic Resource Allocation and Client Scheduling in Hierarchical Federated Learning: A Two-Phase Deep Reinforcement Learning Approach
Federated learning (FL) is a viable technique to train a shared machine learning model
without sharing data. Hierarchical FL (HFL) system has yet to be studied regrading its …
without sharing data. Hierarchical FL (HFL) system has yet to be studied regrading its …
Dynamic Resource Management for Federated Edge Learning With Imperfect CSI: A Deep Reinforcement Learning Approach
S Zhou, L Feng, M Mei, M Yao - IEEE Internet of Things Journal, 2024 - ieeexplore.ieee.org
Federated edge learning (FEL) has become a research hotspot to relieve the computational
burden on servers and protect users' data privacy. In an FEL system, adjusting the client …
burden on servers and protect users' data privacy. In an FEL system, adjusting the client …
面向能量受限工业物联网设备的联邦学习资源管理
范绍帅， 吴剑波， 田辉 - 通信学报, 2022 - infocomm-journal.com
针对工业物联网联邦学习网络中由设备电池能量有限导致的设备失效, 训练中断等问题,
并考虑到无线资源受限的影响, 提出一种动态的多维资源联合管理算法. 首先 …
并考虑到无线资源受限的影响, 提出一种动态的多维资源联合管理算法. 首先 …
A Federated Deep Reinforcement Learning-based Low-power Caching Strategy for Cloud-edge Collaboration
X Zhang, Z Hu, Y Liang, H Xiao, A Xu, M Zheng… - Journal of Grid …, 2024 - Springer
In the era of ubiquitous network devices, an exponential increase in content requests from
user equipment (UE) calls for optimized caching strategies within a cloud-edge integration …
user equipment (UE) calls for optimized caching strategies within a cloud-edge integration …
Adaptive User Scheduling and Resource Allocation in Wireless Federated Learning Networks: A Deep Reinforcement Learning Approach
C Wu, Y Ren, DKC So - ICC 2023-IEEE International …, 2023 - ieeexplore.ieee.org
Federated Learning (FL) is widely regarded as a leading distributed machine learning
paradigm, owing to its outstanding performance in preserving privacy and conserving …
paradigm, owing to its outstanding performance in preserving privacy and conserving …
Scoring Mechanism for Clustering Training in Wireless Federated Learning
Y Wang, C Zhang, E Tong, Y Ni… - 2023 8th International …, 2023 - ieeexplore.ieee.org
Federated learning (FL) is a distributed learning framework, which can be widely applied
into wireless networks to improve network intelligence without disclosing users' private data …
into wireless networks to improve network intelligence without disclosing users' private data …
Providing an improved greedy approach for increasing the number of Served users in cloud-edge networks
M Shirkhani, K Khamforoosh, M Izadbin - Soft Computing Journal, 2022 - scj.kashanu.ac.ir
One of the most significant challenges in edge computing is increasing the number of served
users without changing the complexity of the problem and imposing further delays on cloud …
users without changing the complexity of the problem and imposing further delays on cloud …