2020 IEEE International Conference on Cloud Computing in Emerging Markets (CCEM)最新文献

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Workload Volatility Management in Datacenters and Cloud 数据中心和云中的工作负载波动管理

2020 IEEE International Conference on Cloud Computing in Emerging Markets (CCEM) Pub Date : 2020-11-01 DOI: 10.1109/CCEM50674.2020.00005

Sai Inabattini, Priyanka Gayam

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引用次数: 0

Message from the CCEM 2020 Program Chairs CCEM 2020项目主席致辞

2020 IEEE International Conference on Cloud Computing in Emerging Markets (CCEM) Pub Date : 2020-11-01 DOI: 10.1109/ccem50674.2020.00007

引用次数: 0

Federated K-Means Clustering: A Novel Edge AI Based Approach for Privacy Preservation 联邦k均值聚类:一种基于边缘人工智能的隐私保护新方法

2020 IEEE International Conference on Cloud Computing in Emerging Markets (CCEM) Pub Date : 2020-11-01 DOI: 10.1109/CCEM50674.2020.00021

Hemanth Kumar, Karthik V R, Mydhili K. Nair

{"title":"Federated K-Means Clustering: A Novel Edge AI Based Approach for Privacy Preservation","authors":"Hemanth Kumar, Karthik V R, Mydhili K. Nair","doi":"10.1109/CCEM50674.2020.00021","DOIUrl":"https://doi.org/10.1109/CCEM50674.2020.00021","url":null,"abstract":"Edge AI is the result of a more fundamental thought, “bring code to data, not data to code”. The assumption here is that the data is stored on devices which are computationally powerful and are therefore ‘edge devices’. Our paper deals with the technique of Federated Averaging applied to ‘Federated k-Means’ algorithm. To the best of our knowledge, at the time of writing, there is no published research outcome of applying Federated k-Means, a computing paradigm aligned towards this concept of Edge AI. The data generated at the edge device never leaves it, thus ensuring privacy preservation. Each device has a ML model on it, thus reducing the latency associated with a centralized model deployed on the cloud. Our paper aims to provide a holistic analysis of the performance metrics at the edge devices as well as at the central server after each federation step. Ours is an early publication of the research results, aiming to excite the researcher community on the immense possibilities to explore this new area, a possible solution to the concerns of user data privacy and the inherent latency associated with traditional ML approaches. The findings of our paper can potentially be applied to a privacy-preserving real-world use case, such as EMR (Electronic Medical Record) based ML applications.","PeriodicalId":160355,"journal":{"name":"2020 IEEE International Conference on Cloud Computing in Emerging Markets (CCEM)","volume":"108 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127966241","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: 21

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