Dear colleagues,

I’m sharing the updated CfP of thTX4Nets workshop, with extended submission deadline to April 14. Contributions on uncertainty quantification applied to telecommunication networks are welcome.

Best regards,

Cristina Rottondi

***Please accept our apologies if you receive multiple copies of this call for paper***

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1^st International Workshop on Trustworthy and eXplainable Artificial Intelligence for Networks (TX4Nets) co-located with IFIP Networking 2024
Thessaloniki, Greece, 3–6 June 2024

Link to the official workshop website: https://sites.google.com/view/tx4nets2024
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*** TX4Nets 2024 CALL FOR PAPER ***
link to CFP: https://sites.google.com/view/tx4nets2024/call-for-papers

Aim and Scope

In light of next communication networks characterized by zero-touch network management, network operators have started the deployment of automated AI-based frameworks addressing various use cases including resource allocation, failure prediction and identification, traffic prediction. However, these deployments predominantly function as black boxes, with practitioners unable to comprehend the internal reasoning or decision processes of these automated AI-based frameworks.

In this context, eXplainable Artificial Intelligence (XAI) emerges as a promising collection of frameworks and technologies designed to enhance the transparency of black-box models. XAI achieves this by providing explanations for the decisions made, enabling practitioners to eliminate bias influencing models, understand when to trust or distrust model decisions, and gain insights into the problem at hand. In addition to this, it also becomes of paramount importance to design and adopt models and methods that are inherently able to quantify their uncertainty in taking decisions based on conformal prediction. This, in turn, facilitates a reliable and Trustworthy deployment of machine learning and Artificial Intelligence models.

While the application of Trustworthy and Explainable Artificial Intelligence has been extensively explored in diverse domains such as healthcare and finance, its implementation in communication networks has been relatively sparse, despite being considered of paramount importance. This workshop seeks to address this gap by shedding light on the potential applications of Trustworthy and Explainable AI in achieving transparent AI-based automation for networking. The primary objective of the workshop is to foster collaboration among AI/ML and telecom engineers, facilitating the sharing and exchange of experiences and ideas related to all aspects of trustworthy and explainable AI for network management.

Topics of Interest

The topics of interest for TX4Nets 2024 include, but are not limited to:

• XAI for Model Trustworthiness in Networks
• XAI for Trustworthy Network management
• XAI-driven Network Performance Optimization
• Trustworthiness in AI Models for Communication Networks
• XAI for trustworthy AI in Optical, Wireless, Microwave, B5G/6G Networks
• XAI for Optical, Wireless, or Microwave Networks
• XAI for Autonomous Networks
• XAI for Zero Touch Networks
• XAI for the Edge/Cloud and Internet-of-Things
• XAI for network security, privacy, resilience, reliability, and safety
• XAI Applied to Networking
• XAI Applied to Network Management
• XAI applied to Failure Management
• Security and Explainability in AI for Communication Networks
• Reliability and Explainability in AI for Communication Networks
• Human-in-the-Loop Systems for AI in Communication Networks
• Fair Federated Learning for Communication Networks
• Fair AI-based Resource Allocation in Communication Networks
• Model Uncertainty Quantification and Explainability for Communication Networks
• Model Uncertainty Quantification and Explainability for Network Security
• Conformal Predictions Applied to Communication Networks
• Impact of Adversarial Attacks on Communication Networks AI Model Trustworthiness
• Case Studies and Deployments of XAI in Communication Networks
• XAI for Open RAN in 6G Networks
• AI for trustworthy IoT and Autonomous System Applications
• Ethical Considerations in AI for Communication Networks
• Interoperability and Standards in AI for Communication Networks
• Regulatory Landscape for AI in Communication Networks
• New Business Models for XAI
• Explainable Reinforcement Learning in Communication Networks
• Causal Machine Learning for Networking
• Causal Reinforcement Learning for Networking
• XAI for federated learning-based solutions of 5G/6G and future networks
• XAI for transfer learning-based solutions of 5G/6G and future networks
• XAI for digital twin-based solutions of 5G/6G and future networks

Important Dates
Paper Submission: April 14, 2024 (extended, firm)
Notification of Acceptance: April 27, 2024
Camera-ready Submission: May 6, 2024
Workshop Date: 3 June 2024 (tentative)

Paper Submission

Authors are invited to submit original contributions that have not been published nor has been submitted for publication elsewhere. Papers should be prepared using the IEEE double-column conference style (10pt font) and are limited to 6 pages including references. Papers must be submitted electronically in PDF format on EDAS through this link: https://www.edas.info/newPaper.php?c=31469&track=123080

All papers will be peer reviewed and the comments will be provided to the authors. Once accepted, the paper will be included in the conference proceedings and will be eligible for submission to the IEEE Xplore Digital Library. At least one author of each accepted paper is required to register and present the work in the workshop.

Further information can be found on the official website of TX4Nets 2024: https://sites.google.com/view/tx4nets2024

Workshop Organizers

Omran Ayoub, University of Applied Sciences of Southern Switzerland, Switzerland

Cristina Rottondi, Politecnico di Torino, Italy

Tania Panayiotou, University of Cyprus, Cyprus

Sebastian Troia, Politecnico di Milano, Italy

Marco Savi, University of Milano-Bicocca, Italy