Mobile Information Systems

Deep Learning in Mobile Computing: Architecture, Applications, and Future Challenges


Publishing date
01 Aug 2021
Status
Closed
Submission deadline
19 Mar 2021

Lead Editor
Guest Editors

1Shanghai Polytechnic University, Shanghai, China

2Edinburgh Napier University, Edinburgh, UK

3DAMO Academy, Hangzhou, China

This issue is now closed for submissions.
More articles will be published in the near future.

Deep Learning in Mobile Computing: Architecture, Applications, and Future Challenges

This issue is now closed for submissions.
More articles will be published in the near future.

Description

With the emergence of big data and efficient computing resources, deep learning has made major breakthroughs in many areas of artificial intelligence. However, in the face of increasingly complex tasks, the scale of data and deep learning models has become increasingly large. For example, the amount of tagged image data used to train an image classifier amounts to millions, or even tens of millions. The emergence of large-scale training data provides a material basis for training large models. Therefore, many large-scale machine learning models have emerged in recent years. However, when the training data is increased, the deep learning model may have tens of billions or even hundreds of billions of parameters without any pruning.

In order to improve the training efficiency of the deep learning model and reduce the training time, distributed technology should be used to perform training tasks. At the same time, multiple working nodes are used to train the neural network model with excellent performance in a distributed and efficient manner. Distributed technology is an accelerator of deep learning technology, which can significantly improve the training efficiency of deep learning and further increase its application range. Mobile networks use a distributed architecture, which means that all the computers or devices that are part of it share the workloads in the network. The characteristics of mobile networks make it possible to perform distributed tasks, therefore it can be used for distributed deep learning.

This Special Issue focuses on recent advances in architecture, algorithms, optimization, and models of mobile computing for deep learning tasks. Original research and review articles reflecting various aspects of mobile computing for deep learning are encouraged.

Potential topics include but are not limited to the following:

  • Mobile networking framework for deep learning
  • Fault tolerance in mobile computing systems
  • Algorithms, schemes and techniques in mobile computing systems for deep learning
  • Parallel computing in mobile computing systems for deep learning
  • Optimization and distributed control
  • Distributed infrastructures, parallelization of deep learning training
  • Resource allocation and scheduling of deep learning training
  • Data management of deep learning training
  • Security and privacy in mobile computing systems
  • FPGA based mobile deep learning
  • Edge cloud computing for distributed deep learning
  • Fog computing for distributed deep learning
  • Software platforms and infrastructures for distributed deep learning
  • Application examples and success stories of distributed deep learning
  • Surveys of distributed deep learning at mobile network

Articles

  • Special Issue
  • - Volume 2021
  • - Article ID 6671628
  • - Research Article

Swarm Differential Privacy for Purpose-Driven Data-Information-Knowledge-Wisdom Architecture

Yingbo Li | Yucong Duan | ... | Stelios Fuentes
  • Special Issue
  • - Volume 2021
  • - Article ID 5579451
  • - Research Article

The Traffic Flow Prediction Method Using the Incremental Learning-Based CNN-LTSM Model: The Solution of Mobile Application

Yanli Shao | Yiming Zhao | ... | Jinglong Fang
  • Special Issue
  • - Volume 2021
  • - Article ID 5598988
  • - Research Article

Research on Real-Time Anomaly Detection of Fishing Vessels in a Marine Edge Computing Environment

Jie Huang | Fengwei Zhu | ... | Yongjian Ren
  • Special Issue
  • - Volume 2021
  • - Article ID 6615695
  • - Research Article

Towards Activity Recognition through Multidimensional Mobile Data Fusion with a Smartphone and Deep Learning

Junkuo Cao | Mingcai Lin | ... | Yueshen Xu
  • Special Issue
  • - Volume 2021
  • - Article ID 6683415
  • - Research Article

Active Synchronous Control Strategy of Distributed Power Grid Connection Based on Mobile Network

Liu Zhang | Shaohua Ma | ... | Hao Wang
  • Special Issue
  • - Volume 2021
  • - Article ID 5590963
  • - Research Article

QoS-Based Multicast Routing in Network Function Virtualization-Enabled Software-Defined Mobile Edge Computing Networks

Shimin Sun | Xinchao Zhang | ... | Li Han
  • Special Issue
  • - Volume 2021
  • - Article ID 5578465
  • - Research Article

Efficient Computation Offloading for Service Workflow of Mobile Applications in Mobile Edge Computing

Youwei Yuan | Lu Qian | ... | Qi Zhao
  • Special Issue
  • - Volume 2021
  • - Article ID 6645629
  • - Research Article

Research on Target Tracking Algorithm Based on Siamese Neural Network

Haibo Pang | Qi Xuan | ... | Zhanbo Li
  • Special Issue
  • - Volume 2021
  • - Article ID 6633332
  • - Research Article

BFLP: An Adaptive Federated Learning Framework for Internet of Vehicles

Yongqiang Peng | Zongyao Chen | ... | Jianqiang Ma
  • Special Issue
  • - Volume 2021
  • - Article ID 6630944
  • - Research Article

Air Quality Prediction Based on a Spatiotemporal Attention Mechanism

Xiangyu Zou | Jinjin Zhao | ... | Stelios Fuentes
Mobile Information Systems
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