{"id":1355,"date":"2021-10-10T20:53:54","date_gmt":"2021-10-10T20:53:54","guid":{"rendered":"https:\/\/faculty.engineering.ucdavis.edu\/jzhang\/?page_id=1355"},"modified":"2021-10-10T20:55:24","modified_gmt":"2021-10-10T20:55:24","slug":"mlwins-objectives","status":"publish","type":"page","link":"https:\/\/faculty.engineering.ucdavis.edu\/jzhang\/mlwins-objectives\/","title":{"rendered":"MLWins Objectives"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-page\" data-elementor-id=\"1355\" class=\"elementor elementor-1355\">\n\t\t\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-5017cadb elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"5017cadb\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-50 elementor-top-column elementor-element 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data-widget_type=\"navigation-menu.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t<div class=\"hfe-nav-menu hfe-layout-horizontal hfe-nav-menu-layout horizontal hfe-pointer__double-line hfe-animation__grow\" data-layout=\"horizontal\">\n\t\t\t\t<div role=\"button\" class=\"hfe-nav-menu__toggle elementor-clickable\" tabindex=\"0\" aria-label=\"Menu Toggle\">\n\t\t\t\t\t<span class=\"screen-reader-text\">Menu<\/span>\n\t\t\t\t\t<div class=\"hfe-nav-menu-icon\">\n\t\t\t\t\t\t<i aria-hidden=\"true\"  class=\"fas fa-align-justify\"><\/i>\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<nav class=\"hfe-nav-menu__layout-horizontal hfe-nav-menu__submenu-arrow\" data-toggle-icon=\"&lt;i aria-hidden=&quot;true&quot; tabindex=&quot;0&quot; class=&quot;fas fa-align-justify&quot;&gt;&lt;\/i&gt;\" data-close-icon=\"&lt;i aria-hidden=&quot;true&quot; tabindex=&quot;0&quot; class=&quot;far fa-window-close&quot;&gt;&lt;\/i&gt;\" data-full-width=\"yes\">\n\t\t\t\t\t<ul id=\"menu-1-40975bde\" class=\"hfe-nav-menu\"><li id=\"menu-item-1281\" class=\"menu-item menu-item-type-post_type menu-item-object-page parent hfe-creative-menu\"><a href=\"https:\/\/faculty.engineering.ucdavis.edu\/jzhang\/mlwins\/\" class = \"hfe-menu-item\">MLWiNS<\/a><\/li>\n<li id=\"menu-item-1414\" class=\"menu-item menu-item-type-post_type menu-item-object-page parent hfe-creative-menu\"><a href=\"https:\/\/faculty.engineering.ucdavis.edu\/jzhang\/mlwins-educational-activities\/\" class = \"hfe-menu-item\">Educational Activities<\/a><\/li>\n<li id=\"menu-item-1415\" class=\"menu-item menu-item-type-post_type menu-item-object-page parent hfe-creative-menu\"><a href=\"https:\/\/faculty.engineering.ucdavis.edu\/jzhang\/mlwins-objectives\/\" class = \"hfe-menu-item\">Objectives<\/a><\/li>\n<li id=\"menu-item-1416\" class=\"menu-item menu-item-type-post_type menu-item-object-page parent hfe-creative-menu\"><a href=\"https:\/\/faculty.engineering.ucdavis.edu\/jzhang\/mlwins-outreach\/\" class = \"hfe-menu-item\">Outreach and Broader Impact Outcomes<\/a><\/li>\n<li id=\"menu-item-1417\" class=\"menu-item menu-item-type-post_type menu-item-object-page parent hfe-creative-menu\"><a href=\"https:\/\/faculty.engineering.ucdavis.edu\/jzhang\/mlwins-publications\/\" class = \"hfe-menu-item\">Publications and Code Repositories<\/a><\/li>\n<li id=\"menu-item-1418\" class=\"menu-item menu-item-type-post_type menu-item-object-page parent hfe-creative-menu\"><a href=\"https:\/\/faculty.engineering.ucdavis.edu\/jzhang\/mlwins-tasks\/\" class = \"hfe-menu-item\">Tasks<\/a><\/li>\n<li id=\"menu-item-1419\" class=\"menu-item menu-item-type-post_type menu-item-object-page parent hfe-creative-menu\"><a href=\"https:\/\/faculty.engineering.ucdavis.edu\/jzhang\/mlwins-team-members\/\" class = \"hfe-menu-item\">Team Members<\/a><\/li>\n<\/ul> \n\t\t\t\t<\/nav>\n\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-50 elementor-top-column elementor-element elementor-element-543833c7\" data-id=\"543833c7\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-109650 elementor-widget elementor-widget-text-editor\" data-id=\"109650\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<h2 class=\"h3Style02\">Objectives<\/h2><p class=\"pStyle02\">The past few years have witnessed an explosive growth of Internet of Things (IoT) devices. An earlier report by Cisco predicted that there will be 50 billion connected devices by 2020<sup><a href=\"http:\/\/informationnet.asu.edu\/BLCD_Project\/objectives.html#exactline1\">[1]<\/a><\/sup>, and the number of connected things (including machines, humans, and things) has potential to grow to 500 billions by 2025<sup><a href=\"http:\/\/informationnet.asu.edu\/BLCD_Project\/objectives.html#exactline2\">[2]<\/a><\/sup>. A general consensus is that a high percentage of IoT-created data will have to be stored and analyzed upon close to, or at the network edge<sup><a href=\"http:\/\/informationnet.asu.edu\/BLCD_Project\/objectives.html#exactline3\">[3]<\/a><\/sup>. This has given rise to a new computing paradigm, namely edge computing, which introduces a new architecture by extending cloud computing to the edge of the network so that ultra-low latency can be achieved at the edge.<\/p><p class=\"pStyle02\">It can be observed that many edge networks feature edge devices connected to each other or to a central node wirelessly. The unreliable nature of wireless connectivity, together with constraints in transmission power, bandwidth and computational resources at edge devices, puts forth a significant challenge for the computation, communication and coordination required to learn an accurate model at the network edge. Aiming to tackle this fundamental challenge, this project takes a principled approach to develop an integrated wireless edge learning framework, while taking into account the limitations in edge computing resources and communications in a holistic manner.<\/p><p><strong class=\"h4Style02\">Tasks<\/strong><\/p><ul><li><strong class=\"h5Style02\">Task 1:<\/strong>\u00a0Bandlimited Coordinate Descent: Learning-driven Joint Power Allocation and Gradient Estimation.<\/li><li><strong class=\"h5Style02\">Task 2:<\/strong>\u00a0Bandlimited Gradient Sketching: Power Control and Bandwidth-Accuracy Tradeoffs.<\/li><li><strong class=\"h5Style02\">Task 3:<\/strong>\u00a0Bandlimited Coordinate Descent based on Zero-order Estimates.<\/li><li><strong class=\"h5Style02\">Task 4:<\/strong>\u00a0Bandlimited Gradient Sketching based on Zero-order Methods.<\/li><li><strong class=\"h5Style02\">Task 5:<\/strong>\u00a0Exploiting RF Characteristics in DL-Based CSI Estimation.<\/li><\/ul><p><strong class=\"h4Style02\">Evaluation and Infrastructure:<\/strong><\/p><ul><li><strong class=\"h5Style02\">Datasets:<\/strong><p class=\"pStyle01\">We plan to generate synthetic datasets and to use open database such as MNIST (handwritten digits), CIFAR-10, and ImageNet (images), that are widely adopted in machine learning applications. In order to demonstrate the bandwidth efficiency of our sketching techniques in structured scenarios, we will evaluate algorithms on datasets such as the Criteo 1 TB dataset, and DNA metagenomics dataset, following, e.g., PI Dasarathy&#8217;s work<sup><a href=\"http:\/\/informationnet.asu.edu\/BLCD_Project\/objectives.html#exactline4\">[4]<\/a><\/sup>.<\/p><\/li><li><strong class=\"h5Style02\">Prototype and Experiments:<\/strong><p class=\"pStyle01\">Given the theoretic nature of the proposed research, instead of building a full-fledged prototype, we will implement the proposed algorithms for integrated ML training and wireless networks, using Coral-Dev Boards which include multiple Google Edge TPUs developed to run AI operations at the edge and a GPU server. We will emulate wireless link effects using DSP board in over-the-air computation. For the learning model, we plan to adopt convex ML models such as SVM for assessing the proposed solutions with convergence guarantees on convex objectives. We will evaluate more sophisticated ML problems on the real-world datasets.<\/p><\/li><\/ul><p><strong class=\"h5Style02\">References<\/strong><\/p><p class=\"pStyle02\"><a name=\"exactline1\"><\/a>[1]\u00a0D. Evans, \u201cThe internet of things: How the next evolution of the internet is changing every-thing,\u201d Cisco White Paper, 2011.<\/p><p class=\"pStyle02\"><a name=\"exactline2\"><\/a>[2]\u00a0J. Camhi, \u201cFormer cisco ceo John Chambers predicts 500 billion connected devices by 2025,\u201d Business Insider, 2015.<\/p><p class=\"pStyle02\"><a name=\"exactline3\"><\/a>[3]\u00a0C. MacGillivray, V. Turnera, R. Clarke, J. Feblowitz, K. Knickle, L.Lamy, M. Xiang,A. Siviero, and M. Cansfield, \u201cIDC futurescape: Worldwide internet of things 2016 predic-tions,\u201d IDC FutureScape, 2015.<\/p><p class=\"pStyle02\"><a name=\"exactline4\"><\/a>[4]\u00a0A. Aghazadeh, R. Spring, D. Lejeune, G. Dasarathy, A. Shrivastava, and Richard Baraniuk,\u201cMISSION: Ultra large-scale feature selection using count-sketches,\u201d in Proceedings ofthe 35th International Conference on Machine Learning, ser. Proceedings of Machine Learning Research, J. Dy and A. Krause, Eds., vol. 80. Stockholm Sweden: PMLR, 10\u201315 Jul 2018, pp. 80\u201388. [Online]. Available:\u00a0<a href=\"http:\/\/proceedings.mlr.press\/v80\/aghazadeh18a.html\">http:\/\/proceedings.mlr.press\/v80\/aghazadeh18a.html\u00a0<\/a>.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>MLWiNS Objectives The past few years have witnessed an explosive growth of Internet of Things (IoT) devices. An earlier report by Cisco predicted that there will be 50 billion connected devices by 2020[1], and the number of connected things (including machines, humans, and things) has potential to grow to 500 billions by 2025[2]. A general [&hellip;]<\/p>\n","protected":false},"author":64,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"elementor_header_footer","meta":{"inline_featured_image":false,"footnotes":""},"class_list":["post-1355","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/faculty.engineering.ucdavis.edu\/jzhang\/wp-json\/wp\/v2\/pages\/1355","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/faculty.engineering.ucdavis.edu\/jzhang\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/faculty.engineering.ucdavis.edu\/jzhang\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/faculty.engineering.ucdavis.edu\/jzhang\/wp-json\/wp\/v2\/users\/64"}],"replies":[{"embeddable":true,"href":"https:\/\/faculty.engineering.ucdavis.edu\/jzhang\/wp-json\/wp\/v2\/comments?post=1355"}],"version-history":[{"count":10,"href":"https:\/\/faculty.engineering.ucdavis.edu\/jzhang\/wp-json\/wp\/v2\/pages\/1355\/revisions"}],"predecessor-version":[{"id":1367,"href":"https:\/\/faculty.engineering.ucdavis.edu\/jzhang\/wp-json\/wp\/v2\/pages\/1355\/revisions\/1367"}],"wp:attachment":[{"href":"https:\/\/faculty.engineering.ucdavis.edu\/jzhang\/wp-json\/wp\/v2\/media?parent=1355"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}