-
Notifications
You must be signed in to change notification settings - Fork 1
/
Copy pathcfp.html
140 lines (113 loc) · 7 KB
/
cfp.html
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
<!DOCTYPE html>
<html lang="en-US">
<head>
<meta charset="UTF-8">
<!-- Begin Jekyll SEO tag v2.7.1 -->
<title>Decentralization and Trustworthy Machine Learning in Web3: Methodologies, Platforms, and Applications | Workshop at NeurIPS 2022</title>
<meta name="generator" content="Jekyll v3.9.0" />
<meta property="og:title" content="Decentralization and Trustworthy Machine Learning in Web3: Methodologies, Platforms, and Applications" />
<meta property="og:locale" content="en_US" />
<meta name="description" content="Decentralization and Trustworthy Machine Learning in Web3: Methodologies, Platforms, and Applications | Workshop at NeurIPS 2022" />
<meta property="og:description" content="Decentralization and Trustworthy Machine Learning in Web3: Methodologies, Platforms, and Applications | Workshop at NeurIPS 2022" />
<meta property="og:site_name" content="Decentralization and Trustworthy Machine Learning in Web3: Methodologies, Platforms, and Applications" />
<meta name="twitter:card" content="summary" />
<meta property="twitter:title" content="Decentralization and Trustworthy Machine Learning in Web3: Methodologies, Platforms, and Applications" />
<script type="application/ld+json">
{"headline":"Decentralization and Trustworthy Machine Learning in Web3: Methodologies, Platforms, and Applications","url":"/","name":"Decentralization and Trustworthy Machine Learning in Web3: Methodologies, Platforms, and Applications","description":"Decentralization and Trustworthy Machine Learning in Web3: Methodologies, Platforms, and Applications | Workshop at NeurIPS 2022","@type":"WebSite","@context":"https://schema.org"}</script>
<!-- End Jekyll SEO tag -->
<meta name="viewport" content="width=device-width, initial-scale=1">
<meta name="theme-color" content="#157878">
<meta name="apple-mobile-web-app-status-bar-style" content="black-translucent">
<link rel="stylesheet" href="/DMLW2022/assets/css/style.css?v=">
</head>
<body>
<a id="skip-to-content" href="#content">Skip to the content.</a>
<header class="page-header" role="banner">
<h1 class="project-name">Decentralization and Trustworthy Machine Learning in Web3: <br> Methodologies, Platforms, and Applications</h1>
<h2 class="project-tagline">Workshop at <a href="https://neurips.cc/">NeurIPS 2022</a><br> In-person, December 3, 2022</h2>
<a href="/DMLW2022/" class="btn">Home</a>
<a href="/DMLW2022/cfp" class="btn">Call for Papers</a>
<a href="/DMLW2022/papers" class="btn">Accepted Papers</a>
<a href="/DMLW2022/schedule" class="btn">Tentative Schedule</a>
<a href="/DMLW2022/speakers" class="btn">Speakers</a>
<!-- <a href="/DMLW2022/sponsors" class="btn">Sponsors</a>-->
<!-- <a href="/organizers" class="btn">Organizers</a>-->
<!-- <a href="/committee" class="btn">Program Committee</a>-->
<!-- <a href="/related" class="btn">Related Workshops</a>-->
</header>
<main id="content" class="main-content" role="main">
<h1 id="call-for-papers">Call for Papers</h1>
<style>
.foo {
table-layout: fixed;
width: 100%;
}
</style>
<p><b>We are happy to announce that we will select one best paper award with a 500 USD cash prize, and two runner-up awards with 200 USD cash prize!</b></p>
<p>We invite submissions that have at most 10 pages with unlimited bibliography and appendix.</p>
<p>Topics include but are not limited to:</p>
<ul>
<li>Trustworthy ML systems via decentralization</li>
<li>Privacy-preserving distributed ML systems</li>
<li>Decentralized AI systems for real-world deployment</li>
<li>Decentralized Data management in AI systems</li>
<li>Distributed Consensus & Fault Tolerance Algorithms</li>
<li>Resilient Federated Learning Systems</li>
<li>Decentralized Finance (DeFi) and Decentralized Autonomous Organization (DAO)</li>
<li>Decentralized FL frameworks & benchmarks</li>
<li>Decentralized Data Science</li>
<li>Decentralized training and inference for foundation models</li>
</ul>
Reviewing will be performed in <b>double-blind</b>, with criteria include:
<ul>
<li>Quality of the methodology and experiments</li>
<li>Novelty</li>
<li>Relevance</li>
<li>Societal impacts</li>
</ul>
<h2>Important Dates</h2>
<table class="foo">
<tr>
<td width="50%"><b>Submissions Open</b></td>
<td width="50%">Aug 01, 2022 11:59PM AoE</td>
</tr>
<tr>
<td width="50%"><b>Submission Deadline</b></td>
<td width="50%">Oct 01, 2022 11:59PM AoE</td>
</tr>
<tr>
<td width="50%"><b>Author Notification</b></td>
<td width="50%">Oct 24, 2022 11:59PM AoE</td>
</tr>
<tr>
<td width="50%"><b>Camera Ready and Video Submission</b></td>
<td width="50%">Nov 03, 2022 11:59PM AoE</td>
</tr>
<tr>
<td width="50%"><b>Workshop Dates</b></td>
<td width="50%">Dec 03, 2022</td>
</tr>
</table>
<h2>Author Instructions</h2>
Papers should be submitted to cmt3: <a href="https://cmt3.research.microsoft.com/DMLW2022">https://cmt3.research.microsoft.com/DMLW2022</a>
<h3>Submission Format</h3>
Submitted papers are recommended to have at most <b>10 pages</b> with unlimited bibliography and appendix, using MLWEB3 2022 LaTex style files:
<ul>
<li><a href="https://drive.google.com/file/d/17Txo_DZReOT7No8ANoREuEc8Mwo-tH8N/view?usp=sharing">MLWEB3_2022.tex</a> - LaTeX template</li>
<li><a href="https://drive.google.com/file/d/1uG51XmI5i2s97g8RXbsdz8hxiGXpRUOe/view?usp=sharing">MLWEB3_2022.sty</a> - style file for LaTeX 2e</li>
<li><a href="https://drive.google.com/file/d/1fmKW8jMltxoPY06A2spCNkeT5MGwVmrs/view?usp=sharing">MLWEB3_2022.pdf</a> - example PDF output generated by running "pdflatex"</li>
</ul>
The 10-page limit is not hard, but keeping your paper reasonably short and concise can greatly improve readability. Submissions must be <b>anonymous</b> following <a href="https://nips.cc/Conferences/2022/CallForPapers">NeurIPS double-blind reviewing guidelines</a> and <a href="https://nips.cc/public/CodeOfConduct">NeurIPS Code of Conduct</a>.
Accepted papers will be hosted on this workshop website but are considered non-archival and can be submitted to other workshops, conferences or journals if their submission policy allows.
<h3>Dual submission policy</h3>
We allow dual submission with other workshops or conferences. You can also submit papers that have some overlaps with your recently published papers. However, we <i>disallow</i> papers that are presented at the NeurIPS main conference as well as other machine learning conferences.
<footer class="site-footer">
<span class="site-footer-credits">
<!-- Please contact <a href="mailto:[email protected]">Huan Zhang</a> or <a href="mailto:[email protected]">Linyi Li</a> if you have any questions.<br> -->
If you have any questions, please contact Chejian Xu via <a href="mailto:[email protected]">[email protected]</a>.<br>
<!-- This page was generated by <a href="https://pages.github.com">GitHub Pages</a> following the template of <a href="https://aisecure-workshop.github.io/aml-iclr2021">AML-ICLR 2021 workshop</a>.-->
</span>
</footer>
</main>
</body>
</html>