Connectionists: Call for Papers – Q1 Journal Special Issue on Trustworthy and Responsible Federated Learning

[FRANCA ROCCO DI TORREPADULA]

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Call for Papers
Trustworthy and Responsible Federated Learning
Special Issue in Discover Artificial Intelligence (Q1 Springer Nature)
https://link.springer.com/collections/fdcjaidaif
Open for submissions - Submission deadline: 15 December 2026
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Dear colleagues,
We are pleased to announce that submissions are now open for the Special Issue / Collection on "Trustworthy and Responsible Federated Learning" in Discover Artificial Intelligence (Springer Nature).
We warmly invite you to submit your work and kindly encourage you to share this call with colleagues and interested researchers.
IMPORTANT DATES

  *   Submission deadline: 15 December 2026
  *   Status: Open for submissions


ABOUT THE SPECIAL ISSUE
Federated Learning (FL) has emerged as a key paradigm for privacy-preserving and collaborative machine learning, enabling the training of models across distributed data sources without sharing raw data. FL has shown strong potential in sensitive domains such as healthcare, finance, public services, and industrial systems.
However, recent research highlights that performance alone is insufficient to ensure responsible deployment. Federated systems raise critical challenges related to fairness, robustness, transparency, interpretability, accountability, and regulatory compliance, particularly under adversarial conditions, data and system heterogeneity, and decentralized governance structures.
This Collection focuses on Trustworthy and Responsible Federated Learning, aiming to advance methodological, system-level, and governance-oriented research that ensures FL systems are not only accurate, but also reliable, fair, transparent, and socially aligned.
Building on international frameworks such as the European Commission Ethics Guidelines for Trustworthy AI and the U.S. National Institute of Standards and Technology (NIST) AI Risk Management Framework, the Collection emphasizes the extension of trustworthiness principles to decentralized and collaborative learning settings. It aims to bring together researchers and practitioners to shape the foundations and future directions of trustworthy federated learning.
TOPICS OF INTEREST

  *   Fairness-aware and bias-mitigating FL methods
  *   Privacy- and security-enhancing techniques
  *   Robustness to adversarial and system-level threats
  *   Explainability and interpretability of federated models
  *   Explainable AI in federated learning
  *   Accountability and transparency mechanisms
  *   Compliance with regulatory frameworks such as the European AI Act
  *   Human-in-the-loop and participatory FL
  *   Trust metrics and uncertainty estimation
  *   Decentralized and peer-to-peer FL architectures
  *   Real-world deployment, evaluation, and sustainability considerations


SUBMISSION DETAILS

  *   Participating journal: Discover Artificial Intelligence
  *   Submissions are handled through the Springer Nature Collection page
  *   More information and submission instructions: https://link.springer.com/collections/fdcjaidaif


We look forward to receiving your contributions and to building a strong collection of research on trustworthy and responsible federated learning.
Contact: bruno.fernandes at upe.br<mailto:bruno.fernandes at upe.br> sergio.dimartino at unina.it<mailto:sergio.dimartino at unina.it> mfisichella at l3s.de<mailto:mfisichella at l3s.de> franca.roccoditorrepadula at unina<mailto:franca.roccoditorrepadula at unina> leandro.ssilva at upe.br<mailto:leandro.ssilva at upe.br>

Best regards,
The Guest Editors

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