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Special Session
Aim and Scope
Aim and Scope
In an era dominated by data-driven insights, designing and building trustworthy Computational Intelligence (CI) systems presents fundamental challenges, especially when leveraging data from decentralized sources. Privacy regulations and ethical concerns have raised the demand for CI systems that are both collaborative and reliable, all while upholding stringent standards of data privacy and transparency. Federated Learning (FL) has emerged as a groundbreaking solution to address these challenges. By allowing multiple parties—such as institutions, corporations, or devices—to collaboratively train models without directly sharing their data, FL provides a paradigm shift that enables decentralized learning while preserving data privacy. This approach minimizes the risk of exposing sensitive information and aligns well with evolving regulatory frameworks, such as the GDPR in Europe, which prioritizes data sovereignty.
However, to truly achieve trustworthiness in CI, ensuring privacy is only one piece of the puzzle. Explainability and transparency are equally critical, as they allow end-users and stakeholders to understand and trust the decisions made by these systems. The need for model interpretability becomes particularly complex in a Federated Learning setting, where data resides on diverse, decentralized nodes. Typically, explainability is achieved either by designing inherently interpretable models or through post-hoc interpretability techniques that elucidate model behavior after training. Yet, when these techniques are applied in the FL context, maintaining both privacy and interpretability poses significant technical and ethical challenges.
The aim of this special session is to provide a multidisciplinary and international forum to discuss emerging methodologies for the collaborative learning of CI systems that are both private and trustworthy. This session seeks to explore advanced FL techniques that incorporate robust privacy measures alongside mechanisms for explainability, with the overarching goal of fostering CI models that users and stakeholders can trust. Attendees from various fields—such as machine learning, ethics, law, and policy—are invited to contribute perspectives and solutions that address the unique challenges of building decentralized, privacy-preserving, and interpretable CI systems.
This Special Session is supported by the project “FAIR - Future Artificial Intelligence Research” - Spoke 1 - funded by the European Union under the NextGenerationEU programme and by the CINI (Consorzio Interuniversitario Nazionale per l’Informatica) Data Science Lab.
Topics of Interest
Topics of Interest
The topics of interest include (but are not limited to):
Trustworthy Collaborative Artificial Intelligence
Collaborative/Federated Learning of Explainable Artificial Intelligence Models
Collaborative/Federated Learning of Interpretable by-design models
Collaborative/Federated Learning and post-hoc explainability techniques
Collaborative/Federated Learning of Trustworthy Large Language Models
Collaborative/Federated Supervised, Semi-Supervised and Unsupervised Learning for Fuzzy and Neuro-Fuzzy Models
Collaborative/Federated Supervised, Semi-Supervised and Unsupervised Learning for Neural Networks and Deep Learning Models
Neural Networks and Fuzzy sets theory for Collaborative/Federated Learning
Privacy Preserving Machine Learning
Threats, attacks and defenses to Federated Learning
Applications of Trustworthy Collaborative Artificial Intelligence
Submission and publication info
Submission and publication info
Submitted papers will be peer-reviewed with the same criteria of other WCCI tracks.
The papers accepted for the special session will be included in the WCCI 2026 proceedings and will be published by the IEEE Xplore Digital Library.
Important Dates & Deadlines
Important Dates & Deadlines
January 2026
31 January: Paper submission deadline (23:59, anywhere on Earth, i.e. UTC-12) – no extension will be given!
March 2026
15 March: Paper acceptance notification
April 2026
15 April: Camera-ready papers
June 2026
21 June: Tutorials
24 June: Industry Day
22-26 June: Conference
Committee
Committee
Organizing Committee
Pietro Ducange, University of Pisa, Italy
Michela Fazzolari, Institute of Informatics and Telematics, CNR, Italy
Francesco Marcelloni, University of Pisa, Italy
Fabrizio Ruffini, University of Pisa, Italy
Alessandro Renda, University of Trieste, Italy
Publicity and Communication Chair:
José Luis Corcuera Bárcena, University of Pisa, Italy
Corresponding Chair's E-mail:
pietro.ducange@unipi.it
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