Machine learning systems (MLSys) are emerging in the Internet of Things (IoT) to provision edge intelligence, which is paving our way towards the vision of ubiquitous intelligence. However, despite the maturity of machine learning systems and the IoT, we are facing severe challenges when integrating MLSys and IoT in practical context. For instance, many machine learning systems have been developed for large-scale production (e.g., cloud environments), but IoT introduces additional demands due to heterogeneous and resource-constrained devices and decentralized operation environment. To shed light on this convergence of MLSys and IoT, this paper analyzes the trade-offs by covering the latest developments (up to 2020) on scaling and distributing ML across cloud, edge, and IoT devices. We position machine learning systems as a component of the IoT, and edge intelligence as a socio-technical system. On the challenges of designing trustworthy edge intelligence, we advocate a holistic design approach that takes multi-stakeholder concerns, design requirements and trade-offs into consideration, and highlight the future research opportunities in edge intelligence.
Machine learning (ML) technologies are emerging in the Internet of Things (IoT) to provision intelligent services. This survey moves beyond existing ML algorithms and cloud-driven design to investigate the less-explored systems, scaling and socio-technical aspects for consolidating ML and IoT. It covers the latest developments (up to 2020) on scaling and distributing ML across cloud, edge, and IoT devices. With a multi-layered framework to classify and illuminate system design choices, this survey exposes fundamental concerns of developing and deploying ML systems in the rising cloud-edge-devi…
Beijing University of Posts and Telecommunications（北京邮电大学）
The proliferation of the Internet of Things (IoT) and widespread use of devices with sensing, computing, and communication capabilities have motivated intelligent applications empowered by artificial intelligence. The classical artificial intelligence algorithms require centralized data collection and processing which are challenging in realistic intelligent IoT applications due to growing data privacy concerns and distributed datasets. Federated Learning (FL) has emerged as a distributed privacy-preserving learning framework that enables IoT devices to train global model through sharing model…
University of Nebraska at Kearney（内布拉斯加大学卡尼分校）；Western Michigan University（西密歇根大学）；Hamad Bin Khalifa University（哈马德·本·哈利法大学）；Qatar University（卡塔尔大学）；Information Technology University（信息技术大学）
With Machine Learning (ML) services now used in a number of mission-critical human-facing domains, ensuring the integrity and trustworthiness of ML models becomes all-important. In this work, we consider the paradigm where cloud service providers collect big data from resource-constrained devices for building ML-based prediction models that are then sent back to be run locally on the intermittently-connected resource-constrained devices. Our proposed solution comprises an intelligent polynomial-time heuristic that maximizes the level of trust of ML models by selecting and switching between a s…
University of Oulu（奥卢大学）；Linköping University（林雪平大学）
We consider an IoT sensing network with multiple users, multiple energy harvesting sensors, and a wireless edge node acting as a gateway between the users and sensors. The users request for updates about the value of physical processes, each of which is measured by one sensor. The edge node has a cache storage that stores the most recently received measurements from each sensor. Upon receiving a request, the edge node can either command the corresponding sensor to send a status update, or use the data in the cache. We aim to find the best action of the edge node to minimize the average long-te…
With the advent of powerful, low-cost IoT systems, processing data closer to where the data originates, known as edge computing, has become an increasingly viable option. In addition to lowering the cost of networking infrastructures, edge computing reduces edge-cloud delay, which is essential for mission-critical applications. In this paper, we show the feasibility and study the performance of image classification using IoT devices. Specifically, we explore the relationships between various factors of image classification algorithms that may affect energy consumption such as dataset size, ima…
LUT-Compiled Kolmogorov-Arnold Networks for Lightweight DoS Detection on IoT Edge Devices
Department of Theoretical and Applied Sciences, eCampus University（理论与应用科学系，eCampus大学）；Department of Intelligent Software Systems and Technologies, School of Computer Science and Artificial Intelligence, V.N. Karazin Kharkiv National University（智能软件系统与技术系，计算机科学与人工智能学院，V.N. 卡拉津基尔基夫国立大学）
Distributed deep learning frameworks like federated learning (FL) and its variants are enabling personalized experiences across a wide range of web clients and mobile/IoT devices. However, FL-based frameworks are constrained by computational resources at clients due to the exploding growth of model parameters (eg. billion parameter model). Split learning (SL), a recent framework, reduces client compute load by splitting the model training between client and server. This flexibility is extremely useful for low-compute setups but is often achieved at cost of increase in bandwidth consumption and…
Intrusion Detection on Resource-Constrained IoT Devices with Hardware-Aware ML and DL
Faculty of Sciences, Lebanese University（科学学院，黎巴嫩大学）；DITEN, University of Genova（DITEN，热那亚大学）；IMT Atlantique, Lab-STICC UMR CNRS 6285（IMT Atlantique，Lab-STICC UMR CNRS 6285）；School of Arts and Sciences, Lebanese American University（艺术与科学学院，黎巴嫩美国大学）
Causality Is Key to Understand and Balance Multiple Goals in Trustworthy ML and Foundation Models
CISPA Helmholtz Center for Information Security（CISPA海德堡信息安全中心）；Max Planck Institute for Intelligent Systems, Tübingen（马克斯·普朗克智能系统研究所（图宾根））；Google Research（谷歌研究）；ETH Zürich（苏黎世联邦理工学院）；University of Toronto（多伦多大学）
