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<article itemscope itemtype="http://schema.org/Article"><div class="article__header"><header><h1>EDDL: How do we train neural networks on limited edge devices - PART 1</h1></header><span class="split-space"> </span>
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<i class="far fa-edit"></i></a></div><meta itemprop="headline" content="EDDL: How do we train neural networks on limited edge devices - PART 1"><div class="article__info clearfix"><ul class="left-col menu"><li>
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href="/archive.html?tag=Research">Research</a>
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</li></ul><ul class="right-col menu"><li><i class="far fa-calendar-alt"></i> <span>Oct 13, 2021</span>
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</li></ul></div><meta itemprop="author" content="Pengzhan Hao"/><meta itemprop="datePublished" content="2021-10-13T16:53:20-04:00">
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<div class="article__content" itemprop="articleBody"><p>This post introduces our previous milestone in project “Edge trainer”, as the paper “EDDL: A Distributed Deep Learning System for Resource-limited Edge Computing Environment.” was published.
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As the first part of the introductions, I focus only on the motivation and summary of our works.
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More details in design and implementation can be found in late posts.</p>
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<p><img src="/static/2021-10/edgelearn-1.png" height="250" /></p>
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<h2 id="why-do-we-need-training-on-edge">Why do we need training on edge?</h2>
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<p>Cloud is not trustworthy anymore. More and more facts supports that breach on cloud happens frequently than before.
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Nowadays, with more generated personal sensitive data has been uploaded to the cloud center, tech company know better to someones than user themselves.</p>
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<p>Researchers, no matter in industry on academia, are working in a way that still learning from users’ data but also keeping raw sensitive data under users’ control.
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Many publications already showed feasibility of only sharing after-trained model instead of raw data.
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One recent popular study on this is google’s <a href="https://ai.googleblog.com/2017/04/federated-learning-collaborative.html">federated learning</a>.</p>
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<p>During investigated this problem, we found that let end user train their own data is safe, but sacrifice efficiency.
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Since one end device has limited resources, training time and power consumption can be disappointing.
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We believe there must have a leverage between privacy and efficiency in some target scenarios.</p>
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<p>Fortunately, we observed that users who belongs to the same campus, plant, firm and community always share similar interests.
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Therefore, these co-located users have similar demands in using AI-involved routines.
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Also, co-located users are easily targeted by same type of threats, such as ransomware to financial practitioners.</p>
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<p>Think about this, sending features of a new malware app to cloud services in order to train a neural networks used by antivirus program.
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This process may takes long time and small amount of samples may not be recognized by the global neural networks model.
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With a customized local model trained and deployed on the edge can successfully counter the problem.
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With edge training as a supplement of cloud training can achieve better response time and let the whole system more flexible.</p>
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<h2 id="why-training-on-edge-is-hard">Why training on edge is hard?</h2>
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<p>Since all co-located users’ device can be used for an edge training, issues and challenges occur as deploying this distributed system.</p>
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<p>The first challenge is <strong>struggling workers</strong>.
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Training devices are heterogeneity, from limited IoT camera to high-end media center with powerful GPU.
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They are not designed to do machine learnings.
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So, a good edge-based distributed learning framework must can handle variety speeds in training tasks.</p>
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<p>The second challenge is how to <strong>scale up</strong> clusters.
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In a campus, thousands and more devices may contribute computing resources to the same training tasks.
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However, these devices may located in far not matter in physical or in network topology.
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How can we well use them well, without struggled with endless transmission time remains a challenge.</p>
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<p>The third issue is frequently <strong>joining and exiting</strong> of devices.
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We can’t rely on each devices to faithfully working on training tasks rather than their original workload.
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Smartly schedule work balance and handle join/exit issues also need under consideration.</p>
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<h2 id="our-proposal">Our proposal</h2>
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<ul>
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<li>
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<p>Dynamic training data distribution and runtime profiler</p>
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<p>We design a dynamic training data distribution mechanism that helps to both the first and the third challenges.
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Preprocessing data can be transmitted without leakage of raw sensitive information.
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This can helps with struggling workers who can train small batches in order to upload parameters with a similar training time.
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Also, for extremely slow devices, join and exit of devices cases, dynamic data distribution and profiler can helps with keep global training parameters from polluted and staleness.</p>
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<p>To counter heterogeneity’s, more approaches were applied in our later research.
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More details were introduced to runtime profiler in the later works.</p>
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</li>
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<li>
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<p>Asynchronous and synchronous aggregation enabled</p>
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<p>In our findings, asynchronous and synchronous parameter update have their pros and cons.
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Keeping sync all the time leads struggling worker issue unsolvable.
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However, async’s harm to accuracy and convergence time also need attentions.
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To carefully chose between these two update policies at the runtime is what we proposed to make use of their own advantages.</p>
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||
</li>
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<li>
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<p>Leader role splitting</p>
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||
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<p>The idea is to let worker devices with higher bandwidth taking leader role during training.
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Parameter updating does not require much computation but only need bandwidth.
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||
Devices with sufficient bandwidth can also work as virtual leader devices.
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||
This approach helps with minimize physical devices we used and more leaders can further scale up workers limits.</p>
|
||
</li>
|
||
</ul>
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</div><section class="page__comments d-print-none"></section></article><!-- start custom main bottom snippet -->
|
||
|
||
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|
||
</div>
|
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</div></div></div><div class="page__footer d-print-none">
|
||
<footer class="footer py-4 js-page-footer">
|
||
<div class="main"><div itemscope itemtype="http://schema.org/Person">
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||
<meta itemprop="name" content="Pengzhan Hao"><meta itemprop="url" content="/"><div class="footer__author-links"><div class="author-links">
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||
<ul class="menu menu--nowrap menu--inline"><li title="Send me an Email.">
|
||
<a class="button button--circle mail-button" itemprop="email" href="mailto:haopengzhan@gmail.com" target="_blank">
|
||
<i class="fas fa-envelope"></i>
|
||
</a><li title="Follow me on Github.">
|
||
<a class="button button--circle github-button" itemprop="sameAs" href="https://github.com/codersherlock" target="_blank">
|
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<div class="icon"><svg fill="#000000" width="24px" height="24px" viewBox="0 0 1024 1024" version="1.1" xmlns="http://www.w3.org/2000/svg">
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|
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</svg>
|
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</div>
|
||
</a>
|
||
</li></ul>
|
||
</div>
|
||
</div>
|
||
</div><div class="site-info mt-2">
|
||
<div>© Stop Talking, Start Doing 2021,
|
||
Powered by <a title="Jekyll is a simple, blog-aware, static site generator." href="http://jekyllrb.com/">Jekyll</a> & <a
|
||
title="TeXt is a super customizable Jekyll theme." href="https://github.com/kitian616/jekyll-TeXt-theme">TeXt Theme</a>.
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</footer>
|
||
</div></div>
|
||
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|
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|
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|
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function toc(options) {
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
||
if (scrollTop >= headingsPos[i]) {
|
||
$activeCur = $tocLi.eq(i);
|
||
} else {
|
||
$activeCur || ($activeCur = $tocLi.eq(i));
|
||
break;
|
||
}
|
||
}
|
||
}
|
||
$activeLast && $activeLast.removeClass('active');
|
||
($activeLast = $activeCur).addClass('active');
|
||
}
|
||
function render() {
|
||
if(!hasRendered) {
|
||
$root.append($tocUl);
|
||
$headings.each(function() {
|
||
var $this = $(this);
|
||
$tocUl.append($('<li></li>').addClass('toc-' + $this.prop('tagName').toLowerCase())
|
||
.append($('<a></a>').text($this.text()).attr('href', '#' + $this.prop('id'))));
|
||
});
|
||
$tocLi = $tocUl.children('li');
|
||
$tocUl.on('click', 'a', function(e) {
|
||
e.preventDefault();
|
||
var $this = $(this);
|
||
scrolling = true;
|
||
setState($this.parent());
|
||
$scroller.scrollToAnchor($this.attr('href'), 400, function() {
|
||
scrolling = false;
|
||
});
|
||
});
|
||
}
|
||
hasRendered = true;
|
||
}
|
||
function init() {
|
||
var interval, timeout;
|
||
if(!hasInit) {
|
||
render(); calc(); setState(null, scrolling);
|
||
// run calc every 100 millisecond
|
||
interval = setInterval(function() {
|
||
calc();
|
||
}, 100);
|
||
timeout = setTimeout(function() {
|
||
clearInterval(interval);
|
||
}, 45000);
|
||
window.pageLoad.then(function() {
|
||
setTimeout(function() {
|
||
clearInterval(interval);
|
||
clearTimeout(timeout);
|
||
}, 3000);
|
||
});
|
||
$scrollTarget.on('scroll', function() {
|
||
disabled || setState(null, scrolling);
|
||
});
|
||
$window.on('resize', window.throttle(function() {
|
||
if (!disabled) {
|
||
render(); calc(); setState(null, scrolling);
|
||
}
|
||
}, 100));
|
||
}
|
||
hasInit = true;
|
||
}
|
||
|
||
setOptions(options);
|
||
if (!disabled) {
|
||
init();
|
||
}
|
||
$window.on('resize', window.throttle(function() {
|
||
init();
|
||
}, 200));
|
||
return {
|
||
setOptions: setOptions
|
||
};
|
||
}
|
||
$.fn.toc = toc;
|
||
});
|
||
})();
|
||
/*(function () {
|
||
|
||
})();*/
|
||
</script><script>
|
||
/* toc must before affix, since affix need to konw toc' height. */(function() {
|
||
var SOURCES = window.TEXT_VARIABLES.sources;
|
||
var TOC_SELECTOR = window.TEXT_VARIABLES.site.toc.selectors;
|
||
window.Lazyload.js(SOURCES.jquery, function() {
|
||
var $window = $(window);
|
||
var $articleContent = $('.js-article-content');
|
||
var $tocRoot = $('.js-toc-root'), $col2 = $('.js-col-aside');
|
||
var toc;
|
||
var tocDisabled = false;
|
||
var hasSidebar = $('.js-page-root').hasClass('layout--page--sidebar');
|
||
var hasToc = $articleContent.find(TOC_SELECTOR).length > 0;
|
||
|
||
function disabled() {
|
||
return $col2.css('display') === 'none' || !hasToc;
|
||
}
|
||
|
||
tocDisabled = disabled();
|
||
|
||
toc = $tocRoot.toc({
|
||
selectors: TOC_SELECTOR,
|
||
container: $articleContent,
|
||
scrollTarget: hasSidebar ? '.js-page-main' : null,
|
||
scroller: hasSidebar ? '.js-page-main' : null,
|
||
disabled: tocDisabled
|
||
});
|
||
|
||
$window.on('resize', window.throttle(function() {
|
||
tocDisabled = disabled();
|
||
toc && toc.setOptions({
|
||
disabled: tocDisabled
|
||
});
|
||
}, 100));
|
||
|
||
});
|
||
})();
|
||
(function() {
|
||
var SOURCES = window.TEXT_VARIABLES.sources;
|
||
window.Lazyload.js(SOURCES.jquery, function() {
|
||
var $window = $(window), $pageFooter = $('.js-page-footer');
|
||
var $pageAside = $('.js-page-aside');
|
||
var affix;
|
||
var tocDisabled = false;
|
||
var hasSidebar = $('.js-page-root').hasClass('layout--page--sidebar');
|
||
|
||
affix = $pageAside.affix({
|
||
offsetBottom: $pageFooter.outerHeight(),
|
||
scrollTarget: hasSidebar ? '.js-page-main' : null,
|
||
scroller: hasSidebar ? '.js-page-main' : null,
|
||
scroll: hasSidebar ? $('.js-page-main').children() : null,
|
||
disabled: tocDisabled
|
||
});
|
||
|
||
$window.on('resize', window.throttle(function() {
|
||
affix && affix.setOptions({
|
||
disabled: tocDisabled
|
||
});
|
||
}, 100));
|
||
|
||
window.pageAsideAffix = affix;
|
||
});
|
||
})();
|
||
</script>
|
||
</div>
|
||
<script>(function () {
|
||
var $root = document.getElementsByClassName('root')[0];
|
||
if (window.hasEvent('touchstart')) {
|
||
$root.dataset.isTouch = true;
|
||
document.addEventListener('touchstart', function(){}, false);
|
||
}
|
||
})();
|
||
</script>
|
||
</body>
|
||
</html>
|
||
|