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<p>**************************************************************************<br>
<span style=""> [Please accept our apologies if you receive
multiple copies of this CFP]</span><br>
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<p>5th IEEE Workshop on Pervasive and Resource-constrained
Artificial Intelligence (PeRConAI)<br>
co-located with IEEE PerCom 2026, March 16-20, 2026, Pisa, Italy</p>
<p>Website: <a
href="https://urldefense.com/v3/__http://perconai.iit.cnr.it__;!!D9dNQwwGXtA!QWOqdtRbbS04WTruHR9bBpceE5iCrIJmUEw3q_PXt2ohUb521g4z1EZw4o82NqTzwG9lvtNpDE7ug8Q_2JTauSRHZQ5znRhWG0qnhw$"
target="_blank">http://perconai.iit.cnr.it</a><br>
Email contact for info: <a
href="https://urldefense.com/v3/__https://mailto:perconai@iit.cnr.it__;!!D9dNQwwGXtA!QWOqdtRbbS04WTruHR9bBpceE5iCrIJmUEw3q_PXt2ohUb521g4z1EZw4o82NqTzwG9lvtNpDE7ug8Q_2JTauSRHZQ5znRhL-tLekw$"
target="_blank">perconai@iit.cnr.it</a></p>
<p>The PeRConAI workshop aims at fostering the development and
circulation of new ideas and research directions on pervasive and
resource-constrained machine learning bringing together
practitioners and researchers working on the intersection between
pervasive computing and machine learning, stimulating the
cross-fertilization between the two communities. </p>
<p>The PeRConAI workshop solicits contributions on, but not limited
to, the following topics:</p>
<p><b>Foundations of Advanced Machine learning algorithms and
methods for pervasive systems subject to resource limitations
addressing the following open challenges:</b></p>
<ul>
<li>Distributed/decentralized and collaborative ML for
resource-constrained devices (e.g., resource-efficient federated
learning,</li>
<li>imbalanced data distribution among devices);</li>
<li>Brain- and bio-inspired ML algorithms for pervasive computing
(e.g., Echo State Networks, Liquid State Machines, Spiking
Neural networks);</li>
<li>State-Space Models (SSMs) for resource-constrained devices;</li>
<li>Learning Foundation models at the edge;</li>
<li>Physics-informed ML for efficient training in pervasive
computing,</li>
<li>Continual learning for distributed edge contexts;</li>
<li>Efficient compression of deep learning models for real-time
inference;</li>
<li>Privacy-preserving and robust ML in distributed/decentralized
learning for pervasive and resource-constrained scenarios;</li>
<li>Self- and Semi-supervised learning in pervasive and
resource-constrained scenarios (e.g., energy efficient
generative models);</li>
<li>Contrastive learning in distributed edge environments;</li>
<li>Split learning and Over-the-air computing for
distributed/decentralized learning systems in pervasive and
resource-constrained scenarios;</li>
<li>Pervasive and distributed unlearning methods;</li>
</ul>
<p><b>Applications of Advanced Machine learning algorithms, methods
and approaches for pervasive computing under
resource-limitations applied to the following application
domains:<br>
</b></p>
<ul>
<li>Health and well-being applications (e.g., activity
recognition, health monitoring).</li>
<li>Anomaly/Novelty detection (e.g., Industry 4.0, predictive
maintenance, condition monitoring, intrusion detection, privacy,
and security).</li>
<li>Audio signal processing (e.g., sound event detection, speech
recognition/processing).</li>
<li>Wireless sensing (e.g., mm-wave radars);</li>
<li>Video streams processing on resource-constrained devices.</li>
<li>Natural Language Processing and Information Retrieval (e.g.,
conversational applications running on resource-constrained,
mobile, or edge devices).</li>
<li>Intersection between mobile computing and ML/DL on
resource-constrained devices.</li>
<li>Remote sensing and Earth observation (resource-efficient
satellite edge computing);</li>
<li>AI applications in UAV, e.g., agriculture, logistics, disaster
relief, surveillance, and infrastructure inspection;</li>
<li>Any other real-world applications and case studies wherein the
pervasiveness of resource-constrained devices is central for
knowledge extraction.</li>
</ul>
<p><br>
</p>
<p>Submissions Guidelines<br>
----------------------</p>
<p>Papers, written in IEEE LaTeX or Microsoft Word templates, must
adhere to the formatting instructions specified <a
href="https://www.ieee.org/conferences/publishing/templates">here</a>,
must be 6 pages (10pt font, 2-column format), including text,
figures, and tables. <br>
The submission link is the following: <a
class="moz-txt-link-freetext" href="https://edas.info/N34025">https://edas.info/N34025</a></p>
<p><br>
</p>
<p>Organizing Committee<br>
--------------------<br>
Prof. Plamen Angelov, Lancaster University, UK<br>
Prof. Mario Luca Bernardi, University of Sannio, IT<br>
Dr. Paolo Dini, CTTC, ES<br>
Dr. Franco Maria Nardini, ISTI-CNR, IT<br>
Prof. Riccardo Pecori, eCampus University, IT and IMEM-CNR, IT<br>
Dr. Lorenzo Valerio, IIT-CNR, IT</p>
<div class="moz-signature">-- <br>
<font face="Calibri">
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<p><font face="Calibri"><span style="font-size: 10pt;"><strong>Paolo
Dini</strong> <br>
Researcher (R4)<br>
<span style="font-size: 1pt;"><strong> </strong>
<span style="font-size: 10pt;">Sustainable Artificial
Intelligence (SAI) research unit<br>
Centre Tecnològic de Telecomunicacions de Catalunya
(CTTC)<br>
Av. Carl Friedrich Gauss, 7 - Building B4<br>
08860 - Castelldefels<br>
Tel.: +34 93 645 29 00</span></span></span></font></p>
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