.png){kind=icon}
返回
Secure Coded Computation for Efficient Distributed Learning in Mobile IoT
.png){kind=icon}
PRE
AI
DOI：10.1109/SECON52354.2021.9491589.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Distributed computation plays an essential role in cloud and edge computing. Data such as images, audio, and text can be represented as matrices to facilitate efficient computation, especially in the domains of distributed machine learning, computer vision, and signal processing. Many coded computation algorithms have been proposed for big data applications to securely partition and distribute matrices to parallel worker devices. However, these proposals have yet to he adapted for mobile platforms beyond theoretical means. Mobile IoT networks can greatly benefit from secure distributed computing, however, commercial devices such as smartphones and tablets are much more limited in resources compared to platforms in data centers, requiring special design considerations. We investigate existing distribution schemes from an operational complexity and security viewpoint and study their perlbrmance in several mobile IoT networks, identifying performance bottlenecks in regards to communication and computation costs. From our findings, we propose new, scalable algorithms optimized to handle the unique constraints of mobile loT. Extensive evaluations of our proposals on publicly available image classification datasets show how distributed learning can be specially optimized to enhance runtime and battery performance on mobile loT by over 10x.
Keyword:
Distributed computing
coded computations
edge device
mobile IoT
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
机构
引用论文
Ultrastructural specialization at the host-pathogen interface in rust-infected flax.png)
Protoplasma
ProtoplasmaIF0
PREAI
分享收藏
Synthesis of Cell Wall Microfibrils in vitro by a "Soluble" Chitin Synthetase from
Mucor rouxiiScience
ScienceIF0
PREAI
分享收藏
查看更多内容