End-edge-cloud collaborative computing for deep learning: A comprehensive survey
The booming development of deep learning applications and services heavily relies on
large deep learning models and massive data in the cloud. However, cloud-based deep …
large deep learning models and massive data in the cloud. However, cloud-based deep …
Deep learning challenges and prospects in wireless sensor network deployment
Y Qiu, L Ma, R Priyadarshi - Archives of Computational Methods in …, 2024 - Springer
This paper explores the transformative integration of deep learning applications in the
deployment of Wireless Sensor Networks (WSNs). As WSNs continue to play a pivotal role in …
deployment of Wireless Sensor Networks (WSNs). As WSNs continue to play a pivotal role in …
Fedasmu: Efficient asynchronous federated learning with dynamic staleness-aware model update
As a promising approach to deal with distributed data, Federated Learning (FL) achieves
major advancements in recent years. FL enables collaborative model training by exploiting …
major advancements in recent years. FL enables collaborative model training by exploiting …
Computation-efficient deep learning for computer vision: A survey
Over the past decade, deep learning models have exhibited considerable advancements,
reaching or even exceeding human-level performance in a range of visual perception tasks …
reaching or even exceeding human-level performance in a range of visual perception tasks …
An intelligent resource management method in SDN based fog computing using reinforcement learning
M Anoushee, M Fartash, J Akbari Torkestani - Computing, 2024 - Springer
Nowadays, cloud computing faces growing challenges, furthermore, responding to time-
sensitive requests in the traditional cloud computing model is one of the major challenges …
sensitive requests in the traditional cloud computing model is one of the major challenges …
SIM-FED: Secure IoT malware detection model with federated learning
Many IoT devices are presently in use without sufficient security measures. The vulnerability
of these devices to malware highlights the necessity for effective methods to identify …
of these devices to malware highlights the necessity for effective methods to identify …
Talaria: Interactively optimizing machine learning models for efficient inference
On-device machine learning (ML) moves computation from the cloud to personal devices,
protecting user privacy and enabling intelligent user experiences. However, fitting models …
protecting user privacy and enabling intelligent user experiences. However, fitting models …
Enhanced Biometric Template Protection Schemes for Securing Face Recognition in IoT Environment
With the increasing use of biometrics in Internet of Things (IoT) based applications, it is
essential to ensure that biometric-based authentication systems are secure. Biometric …
essential to ensure that biometric-based authentication systems are secure. Biometric …
Internet of Things (IoT) in digital agriculture: An overview
Climate change, land degradation, and limited land and water resources have challenged
our ability to meet the food demand of a rapidly growing population. To tackle this challenge …
our ability to meet the food demand of a rapidly growing population. To tackle this challenge …
[HTML][HTML] An open source IoT edge-computing system for monitoring energy consumption in buildings
Abstract This paper presents" An Open IoT Edge Computing System for Monitoring Energy
Consumption in Buildings." Implemented at the Faculty of Electromechanical Engineering of …
Consumption in Buildings." Implemented at the Faculty of Electromechanical Engineering of …