CFP: 5th IEEE Workshop on Pervasive and Resource-constrained Artificial Intelligence (PeRConAI’26)
Paolo Dini
[Please accept our apologies if you receive multiple copies of this CFP]
**************************************************************************
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
Email contact for info: perc...@iit.cnr.it
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,
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