This workshop focuses on how to address unique challenges during applying AI/ML in IoT. It invites researchers and practitioners to submit papers describing original work or experiences related to the entire life-cycle of an IoT powered by AI and ML, specifically federated learning and meta-learning. Federated learning and meta-learning techniques for IoT systems are the two main pivotal topics of this workshop. The uniqueness of our workshop is that it focuses on realistic problems that exist throughout the life-cycle of AI/ML-powered IoT. Many problems have not been well addressed in existing conferences or workshops. For example, most existing work only addresses how to develop good AI/ML models while assuming that a centralized dataset already exists. We hope to bridge this gap with our workshop. Also, most studies assume there exists a sufficient training dataset, while in real-world scenarios there are many limitations on available datasets.
Artificial intelligence (AI) and machine learning (ML) are key enabling technologies for many Internet of Things (IoT) applications and meta-learning. However, the collection and processing of data for AI and ML is very challenging in the IoT domain, even learning from data is critical in meta-learning and federated learning. This special session aims to bring together researchers from such domains and topics for this workshop include, but are not limited to:
Techniques for making use of data collected by geographically dispersed sensors to provide useful services through AI/ML
Techniques for sharing data and training AI/ML models while preserving user sensitive information
Techniques for dealing with noisy data and labels
Techniques for reducing human effort in data labeling (such as active learning)
Techniques for evolving from a new system that is initially trained with only a small amount of data
Automated Learning
Meta-learning
Efficient data analytics
Distributed learning
Federated learning and its applications
Efficient learning on IoT devices
Collaborative learning
Submission Deadline: August 16, 2021
Notification of Acceptance: September 13, 2021
Camera-ready papers & Pre-Registration: October 11, 2021
Papers submitted for reviewing should conform to IEEE specifications. Manuscript templates can be downloaded from IEEE website. The maximum length of papers is 8 pages. All the papers will go through double-blind peer review process. Authors’ names and affiliations should not appear in the submitted paper. Authors’ prior work should be cited in the third person. Authors should also avoid revealing their identities and/or institutions in the text, figures, links, etc.
Papers must be submitted via the CMT System by selecting the track “Special Session on Advanced Machine Learning and Applications: Federated Learning and Meta-Learning”. All accepted papers must be presented by one of the authors, who must register. Detailed instructions for submitting papers can be found at How to Submit.
Accepted papers will be published in the ICMLA 2021 conference proceedings (to be published by IEEE).
Hamid Reza Arabnia
University of Georgia
Panos Pardalos
University of Florida
Mehdi Bennis
University of Oulu
Javad Heidari
LG Silicon Valley Lab
Praveen Palanisamy
Senior AI Engineer, Microsoft
Kin K. Leung
Imperial College
Mohammad Mozaffari
Ericsson Research
Mohammad Shaqfeh
Texas A&M University at Qatar
Javad Mohammadi
Carnegie Mellon University
Mingzhe Chen
Princeton University
Abbas Khosravi
Deakin University
Sakshi Mishra
University of British Columbia
Urmish Thakker
SambaNova Systems
Jihong Park
Deakin University
Parinaz Naghizadeh
The Ohio State University
Shahab Bahrami
The University of British Columbia
Vahid Akbari
University of Nottingham
Marzieh Khakifirooz
Tecnológico de Monterrey
Soheyla Amirian
University of Georgia
Mostafa Mirshekari
Stanford University
Victor Valls Delgado
Yale University
Farid Ghareh Mohammadi
University of Georgia
Ahmed Imteaj
Florida International University
Ali Tazarv
UC Irvine
Khandakar Mamun Ahmed
Florida International University
Farzan Shenavarmasouleh
University of Georgia