2022 IEEE International Conference on Edge Computing and Communications (EDGE)最新文献

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Bring Trust to Edge: Secure and Decentralized IoT Framework with BFT and Permissioned Blockchain 将信任带到边缘:具有BFT和许可区块链的安全和分散的物联网框架

2022 IEEE International Conference on Edge Computing and Communications (EDGE) Pub Date : 2022-07-01 DOI: 10.1109/EDGE55608.2022.00025

Yusen Wu, Jinghui Liao, P. Nguyen, Weisong Shi, Y. Yesha

{"title":"Bring Trust to Edge: Secure and Decentralized IoT Framework with BFT and Permissioned Blockchain","authors":"Yusen Wu, Jinghui Liao, P. Nguyen, Weisong Shi, Y. Yesha","doi":"10.1109/EDGE55608.2022.00025","DOIUrl":"https://doi.org/10.1109/EDGE55608.2022.00025","url":null,"abstract":"While our society accelerates its transition to the Internet of Things, billions of IoT devices are now linked to the network. While these gadgets provide enormous convenience, they generate a large amount of data that has already beyond the network’s capacity. To make matters worse, the data acquired by sensors on such IoT devices also include sensitive user data that must be appropriately treated. At the moment, the answer is to provide hub services for data storage in data centers. However, when data is housed in a centralized data center, data owners lose control of the data, since data centers are centralized solutions that rely on data owners’ faith in the service provider. In addition, edge computing enables edge devices to collect, analyze, and act closer to the data source, the challenge of data privacy near the edge is also a tough nut to crack.A large number of user information leakage both for IoT hub and edge made the system untrusted all along. Accordingly, building a decentralized IoT system near the edge and bringing real trust to the edge is indispensable and significant. To eliminate the need for a centralized data hub, we present a prototype of a unique, secure, and decentralized IoT framework called Reja, which is built on a permissioned Blockchain and an intrusion-tolerant messaging system ChiosEdge, and the critical components of ChiosEdge are reliable broadcast and BFT consensus. We evaluated the latency and throughput of Reja and its sub-module ChiosEdge.","PeriodicalId":274131,"journal":{"name":"2022 IEEE International Conference on Edge Computing and Communications (EDGE)","volume":"398 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129184599","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

CONTINUER: Maintaining Distributed DNN Services During Edge Failures continuation:在边缘故障期间维护分布式DNN服务

2022 IEEE International Conference on Edge Computing and Communications (EDGE) Pub Date : 2022-04-25 DOI: 10.1109/EDGE55608.2022.00029

A. Majeed, Peter Kilpatrick, I. Spence, B. Varghese

{"title":"CONTINUER: Maintaining Distributed DNN Services During Edge Failures","authors":"A. Majeed, Peter Kilpatrick, I. Spence, B. Varghese","doi":"10.1109/EDGE55608.2022.00029","DOIUrl":"https://doi.org/10.1109/EDGE55608.2022.00029","url":null,"abstract":"Partitioning and deploying Deep Neural Networks (DNNs) across edge nodes may be used to meet performance objectives of applications. However, the failure of a single node may result in cascading failures that will adversely impact the delivery of the service and will result in failure to meet specific objectives. The impact of these failures needs to be minimised at runtime. Three techniques are explored in this paper, namely repartitioning, early-exit and skip-connection. When an edge node fails, the repartitioning technique will repartition and redeploy the DNN thus avoiding the failed nodes. The early-exit technique makes provision for a request to exit (early) before the failed node. The skip connection technique dynamically routes the request by skipping the failed nodes. This paper will leverage trade-offs in accuracy, end-to-end latency and downtime for selecting the best technique given user-defined objectives (accuracy, latency and downtime thresholds) when an edge node fails. To this end, CONTINUER is developed. Two key activities of the framework are estimating the accuracy and latency when using the techniques for distributed DNNs and selecting the best technique. It is demonstrated on a lab-based experimental testbed that CONTINUER estimates accuracy and latency when using the techniques with no more than an average error of 0.28% and 13.06%, respectively, and selects the suitable technique with a low overhead of no more than 16.82 milliseconds and an accuracy of up to 99.86%.","PeriodicalId":274131,"journal":{"name":"2022 IEEE International Conference on Edge Computing and Communications (EDGE)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134187989","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Robustness and Personalization in Federated Learning: A Unified Approach via Regularization 联邦学习中的鲁棒性和个性化:一种基于正则化的统一方法

2022 IEEE International Conference on Edge Computing and Communications (EDGE) Pub Date : 2020-09-14 DOI: 10.1109/EDGE55608.2022.00014

Achintya Kundu, Pengqian Yu, L. Wynter, Shiau Hong Lim

{"title":"Robustness and Personalization in Federated Learning: A Unified Approach via Regularization","authors":"Achintya Kundu, Pengqian Yu, L. Wynter, Shiau Hong Lim","doi":"10.1109/EDGE55608.2022.00014","DOIUrl":"https://doi.org/10.1109/EDGE55608.2022.00014","url":null,"abstract":"We present a class of methods for robust, personalized federated learning, called Fed+, that unifies many federated learning algorithms. The principal advantage of this class of methods is to better accommodate the real-world characteristics found in federated training, such as the lack of IID data across parties, the need for robustness to outliers or stragglers, and the requirement to perform well on party-specific datasets. We achieve this through a problem formulation that allows the central server to employ robust ways of aggregating the local models while keeping the structure of local computation intact. Without making any statistical assumption on the degree of heterogeneity of local data across parties, we provide convergence guarantees for Fed+ for convex and non-convex loss functions under different (robust) aggregation methods. The Fed+ theory is also equipped to handle heterogeneous computing environments including stragglers without additional assumptions; specifically, the convergence results cover the general setting where the number of local update steps across parties can vary. We demonstrate the benefits of Fed+ through extensive experiments across standard benchmark datasets.","PeriodicalId":274131,"journal":{"name":"2022 IEEE International Conference on Edge Computing and Communications (EDGE)","volume":"106 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128157367","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

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