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:

Topics of Interests
Techniques:
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

Learning paradigms:
Meta-learning

Efficient data analytics

Distributed learning

Federated learning and its applications

Efficient learning on IoT devices

Collaborative learning

Important Dates:
Submission Deadline: August 30, 2020

Notification of Acceptance: September 20, 2020

Camera-ready papers & Pre-Registration: October 1, 2020





Paper Publication:
Accepted papers will be published in the ICMLA 2020 conference proceedings (to be published by IEEE).


Workshop Chairs
M. Hadi Amini, Florida International University

Shiqiang Wang, IBM T. J. Watson Research Center