Connectionists: CFP: 5th IEEE Workshop on Pervasive and Resource-constrained Artificial Intelligence (PeRConAI’26)

[Paolo Dini]

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   [Please accept our apologies if you receive multiple copies of this CFP]
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5th IEEE Workshop on Pervasive and Resource-constrained Artificial 
Intelligence (PeRConAI)
co-located with IEEE PerCom 2026, March 16-20, 2026, Pisa, Italy

Website: http://perconai.iit.cnr.it 
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Email contact for info: perconai at iit.cnr.it 
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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.

The PeRConAI workshop solicits contributions on, but not limited to, the 
following topics:

*Foundations of Advanced Machine learning algorithms and methods for 
pervasive systems subject to resource limitations addressing the 
following open challenges:*

  * Distributed/decentralized and collaborative ML for
    resource-constrained devices (e.g., resource-efficient federated
    learning,
  * imbalanced data distribution among devices);
  * Brain- and bio-inspired ML algorithms for pervasive computing (e.g.,
    Echo State Networks, Liquid State Machines, Spiking Neural networks);
  * State-Space Models (SSMs) for resource-constrained devices;
  * Learning Foundation models at the edge;
  * Physics-informed ML for efficient training in pervasive computing,
  * Continual learning for distributed edge contexts;
  * Efficient compression of deep learning models for real-time inference;
  * Privacy-preserving and robust ML in distributed/decentralized
    learning for pervasive and resource-constrained scenarios;
  * Self- and Semi-supervised learning in pervasive and
    resource-constrained scenarios (e.g., energy efficient generative
    models);
  * Contrastive learning in distributed edge environments;
  * Split learning and Over-the-air computing for
    distributed/decentralized learning systems in pervasive and
    resource-constrained scenarios;
  * Pervasive and distributed unlearning methods;

*Applications of Advanced Machine learning algorithms, methods and 
approaches for pervasive computing under resource-limitations applied to 
the following application domains:
*

  * Health and well-being applications (e.g., activity recognition,
    health monitoring).
  * Anomaly/Novelty detection (e.g., Industry 4.0, predictive
    maintenance, condition monitoring, intrusion detection, privacy, and
    security).
  * Audio signal processing (e.g., sound event detection, speech
    recognition/processing).
  * Wireless sensing (e.g., mm-wave radars);
  * Video streams processing on resource-constrained devices.
  * Natural Language Processing and Information Retrieval (e.g.,
    conversational applications running on resource-constrained, mobile,
    or edge devices).
  * Intersection between mobile computing and ML/DL on
    resource-constrained devices.
  * Remote sensing and Earth observation (resource-efficient satellite
    edge computing);
  * AI applications in UAV, e.g., agriculture, logistics, disaster
    relief, surveillance, and infrastructure inspection;
  * Any other real-world applications and case studies wherein the
    pervasiveness of resource-constrained devices is central for
    knowledge extraction.


Submissions Guidelines
----------------------

Papers, written in IEEE LaTeX or Microsoft Word templates, must adhere 
to the formatting instructions specified here 
<https://www.ieee.org/conferences/publishing/templates>, must be 6 pages 
(10pt font, 2-column format), including text, figures, and tables.
The submission link is the following: https://edas.info/N34025


Organizing Committee
--------------------
Prof. Plamen Angelov, Lancaster University, UK
Prof. Mario Luca Bernardi, University of Sannio, IT
Dr. Paolo Dini, CTTC, ES
Dr. Franco Maria Nardini, ISTI-CNR, IT
Prof. Riccardo Pecori, eCampus University, IT and IMEM-CNR, IT
Dr. Lorenzo Valerio, IIT-CNR, IT

-- 

*Paolo Dini*
Researcher (R4)
** Sustainable Artificial Intelligence (SAI) research unit
Centre Tecnològic de Telecomunicacions de Catalunya (CTTC)
Av. Carl Friedrich Gauss, 7 - Building B4
08860 - Castelldefels
Tel.: +34 93 645 29 00

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