Track Description

With the increasing amount of data worldwide, centralized machine learning techniques face several challenges, such as privacy and bandwidth limitations. To overcome this, edge and fog computing paradigms are providing the underlying foundations to support modern distributed machine learning applications. Currently, there are growing efforts by academic and industry researchers to develop solutions that can effectively exploit edge and fog deployments for training and inference of AI models. While equipping AI models with trustworthy aspects (explainability, interpretability, transparency, among others) is an emerging domain to scale the use of AI within classical centralized applications, the inherent distributed characteristics of edge and fog deployments introduce a higher level of complexity to adopting AI in distributed environments. This track solicits contributions addressing trustworthy issues that foster the adoption of AI models in edge and fog computing systems and applications.

Topics of interest include

* Theoretical and/or practical methods to assess trustworthy aspects of AI models in distributed environments
* Augmentation of distributed AI models with trustworthy mechanisms
* Distributed embedding methods to improve standard machine learning pipelines with trustworthy capabilities
* Lessons learned and experiences from developed trustworthy edge and fog applications
* Social, economic, and behavioral considerations for adopting trustworthy edge and fog solutions.
* Efficient learning and AI model optimization for resource-constrained / IoT devices.
* Defense mechanisms against adversarial machine learning in mobile edge networks.
* Deployment strategies of FMEC Servers

Track Chairs and TPC Members

* Huber Flores, University of Tartu, Estonia (Chair)
* Nikolay Tcholtchev, FOKUS, Germany
* Samuel Marchal, Aalto University, Finland
* Madhusanka Liyanage, UCD, Ireland
* Aaron Di Ying, TUDelft, Netherlands
* Mehrdad Asadi, University of Tartu, Estonia
* Souneil Park, Telefonica, Spain.