Heterogeneous Workload based Consumer Resource Recommendation Model for Smart Cities: eHealth Edge-Cloud Connectivity Using Federated Split Learning
ST Ahmed, J Jeong - IEEE Transactions on Consumer …, 2024 - ieeexplore.ieee.org
Over the past decade, there has been a significant surge in consumer application services
and server connectivity, and this trend is expected to double in 2030. The primary …
and server connectivity, and this trend is expected to double in 2030. The primary …
FedOPT: federated learning-based heterogeneous resource recommendation and optimization for edge computing
Resource recommendation in edge computing relies on distributed resource alignment
across multiple servers and interconnected networks. Consequently, addressing issues …
across multiple servers and interconnected networks. Consequently, addressing issues …
Federated deep reinforcement learning for recommendation-enabled edge caching in mobile edge-cloud computing networks
To support rapidly increasing services and applications from users, multi-tier computing is
emerged as a promising system-level computing architecture by distributing …
emerged as a promising system-level computing architecture by distributing …
A Collaborative Migration Algorithm for Edge Services Based on Evolutionary Reinforcement Learning
Y Zuo, X Zhang, B Zhang, Z Cao - International Conference on Algorithms …, 2023 - Springer
Multi-access edge computing (MEC) enables users' smart devices to execute computing-
intensive and delay-sensitive applications by sinking computing power to edge servers …
intensive and delay-sensitive applications by sinking computing power to edge servers …
Mobile healthcare data mining for sport item recommendation in edge-cloud collaboration
C Chen, C Li, Y Duan - Wireless Networks, 2022 - Springer
With the continuous maturity and adoption of mobile devices enabled by wireless
communication technology, people are more apt to record their sport exercise data or …
communication technology, people are more apt to record their sport exercise data or …
RALaaS: Resource-aware learning-as-a-service in edge-cloud collaborative smart connected communities
As increasingly advanced data collection and computing abilities are equipped by devices
at the network edge, accompanying the vigorous development of machine learning, edge …
at the network edge, accompanying the vigorous development of machine learning, edge …
Data-driven optimization for cooperative edge service provisioning with demand uncertainty
Multiaccess edge computing (MEC) empowers service providers (SPs) to run applications
on the shared edge platforms in close proximity to mobile users, enabling ultralow latency …
on the shared edge platforms in close proximity to mobile users, enabling ultralow latency …
Digital twin-assisted federated learning service provisioning over mobile edge networks
Federated Learning (FL) offers collaborative machine learning without data exposure, but
challenges arise in the mobile edge network (MEC) environment due to limited resources …
challenges arise in the mobile edge network (MEC) environment due to limited resources …
Proactive Recommendation of Composite Services in Multi-Access Edge Computing
Z Liu, QZ Sheng, D Chu, X Xu… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Multi-Access Edge Computing (MEC) is an emerging computing paradigm that brings
services from centralized cloud to nearby network edge to improve users' Quality of …
services from centralized cloud to nearby network edge to improve users' Quality of …
ESFL: Efficient Split Federated Learning over Resource-Constrained Heterogeneous Wireless Devices
Federated learning (FL) allows multiple parties (distributed devices) to train a machine
learning model without sharing raw data. How to effectively and efficiently utilize the …
learning model without sharing raw data. How to effectively and efficiently utilize the …