Journal ArticleDOI
AI-Enhanced Offloading in Edge Computing: When Machine Learning Meets Industrial IoT
Wen Sun,Jiajia Liu,Yanlin Yue +2 more
TLDR
An AI enhanced offloading framework is proposed for service accuracy maximization, which considers service accuracy as a new metric besides delay, and intelligently disseminates the traffic to edge servers or through an appropriate path to remote cloud.Abstract:
The Industrial Internet of Things (IIoT) enables intelligent industrial operations by incorporating artificial intelligence (AI) and big data technologies. An AI-enabled framework typically requires prompt and private cloud-based service to process and aggregate manufacturing data. Thus, integrating intelligence into edge computing is without doubt a promising development trend. Nevertheless, edge intelligence brings heterogeneity to the edge servers, in terms of not only computing capability, but also service accuracy. Most works on offloading in edge computing focus on finding the power-delay trade-off, ignoring service accuracy provided by edge servers as well as the accuracy required by IIoT devices. In this vein, in this article we introduce an intelligent computing architecture with cooperative edge and cloud computing for IIoT. Based on the computing architecture, an AI enhanced offloading framework is proposed for service accuracy maximization, which considers service accuracy as a new metric besides delay, and intelligently disseminates the traffic to edge servers or through an appropriate path to remote cloud. A case study is performed on transfer learning to show the performance gain of the proposed framework.read more
Citations
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Journal ArticleDOI
Federated Learning With Differential Privacy: Algorithms and Performance Analysis
Kang Wei,Jun Li,Ming Ding,Chuan Ma,Howard H. Yang,Farhad Farokhi,Shi Jin,Tony Q. S. Quek,H. Vincent Poor +8 more
TL;DR: Wang et al. as mentioned in this paper proposed a novel framework based on the concept of differential privacy, in which artificial noise is added to parameters at the clients' side before aggregating, namely, noising before model aggregation FL (NbAFL).
Posted Content
Federated Learning with Differential Privacy: Algorithms and Performance Analysis
Kang Wei,Jun Li,Ming Ding,Chuan Ma,Howard H. Yang,Farokhi Farhad,Shi Jin,Tony Q. S. Quek,H. Vincent Poor +8 more
TL;DR: A novel framework based on the concept of differential privacy, in which artificial noise is added to parameters at the clients’ side before aggregating, namely, noising before model aggregation FL (NbAFL), is proposed and an optimal convergence bound is found that achieves the best convergence performance at a fixed privacy level.
Journal ArticleDOI
AI for Next Generation Computing: Emerging Trends and Future Directions
Sukhpal Singh Gill,Minxian Xu,Carlo Ottaviani,Panos Patros,Rami Bahsoon,Arash Shaghaghi,Muhammed Golec,Vlado Stankovski,Huaming Wu,Ajit Varghese Abraham,Manmeet Singh,Harshit Mehta,Soumya Ghosh,Thar Baker,Ajith Kumar Parlikad,Hanan Lutfiyya,Salil S. Kanhere,Rizos Sakellariou,Schahram Dustdar,Omer Rana,Ivona Brandic,Steve Uhlig +21 more
TL;DR: In this article , the authors discuss challenges and opportunities for leveraging AI and ML in next generation computing for emerging computing paradigms, including cloud, fog, edge, serverless and quantum computing environments.
Journal ArticleDOI
Reducing Offloading Latency for Digital Twin Edge Networks in 6G
TL;DR: A new vision of Digital Twin Edge Networks (DITEN) where digital twins of edge servers estimate edge servers’ states and DT of the entire MEC system provides training data for offloading decision is presented, which effectively diminishes the average offloading latency, the offloading failure rate, and the service migration rate, while saving the system cost with DT assistance.
Journal ArticleDOI
Ten Years of Industrie 4.0
TL;DR: A decade after its introduction, Industrie 4.0 has been established globally as the dominant paradigm for the digital transformation of the manufacturing industry as mentioned in this paper , which is the basis for data-based value creation, innovative business models, and agile forms of organization.
References
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Journal ArticleDOI
Learning IoT in Edge: Deep Learning for the Internet of Things with Edge Computing
He Li,Kaoru Ota,Mianxiong Dong +2 more
TL;DR: This article first introduces deep learning for IoTs into the edge computing environment, and designs a novel offloading strategy to optimize the performance of IoT deep learning applications with edge computing.
Proceedings ArticleDOI
When Edge Meets Learning: Adaptive Control for Resource-Constrained Distributed Machine Learning
Shiqiang Wang,Tiffany Tuor,Theodoros Salonidis,Kin K. Leung,Christian Makaya,Ting He,Kevin S. Chan +6 more
TL;DR: This paper analyzes the convergence rate of distributed gradient descent from a theoretical point of view, and proposes a control algorithm that determines the best trade-off between local update and global parameter aggregation to minimize the loss function under a given resource budget.
Journal ArticleDOI
Software-Defined Networks with Mobile Edge Computing and Caching for Smart Cities: A Big Data Deep Reinforcement Learning Approach
TL;DR: An integrated framework that can enable dynamic orchestration of networking, caching, and computing resources to improve the performance of applications for smart cities is proposed and a novel big data deep reinforcement learning approach is presented.
Posted Content
When Edge Meets Learning: Adaptive Control for Resource-Constrained Distributed Machine Learning
Shiqiang Wang,Tiffany Tuor,Theodoros Salonidis,Kin K. Leung,Christian Makaya,Ting He,Kevin S. Chan +6 more
TL;DR: In this paper, the authors consider the problem of learning model parameters from data distributed across multiple edge nodes, without sending raw data to a centralized place, and propose a control algorithm that determines the best trade-off between local update and global parameter aggregation to minimize the loss function under a given resource budget.
Journal ArticleDOI
On Removing Routing Protocol from Future Wireless Networks: A Real-time Deep Learning Approach for Intelligent Traffic Control
Fengxiao Tang,Bomin Mao,Zubair Md. Fadlullah,Nei Kato,Osamu Akashi,Takeru Inoue,Kimihiro Mizutani +6 more
TL;DR: This article proposes a new, real-time deep learning based intelligent network traffic control method, exploiting deep Convolutional Neural Networks (deep CNNs) with uniquely characterized inputs and outputs to represent the considered Wireless Mesh Network (WMN) backbone.