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.
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
Meta-learning
Efficient data analytics
Distributed learning
Federated learning and its applications
Efficient learning on IoT devices
Collaborative learning
Submission Deadline: August 6, 2020 August 30, 2020
Notification of Acceptance: September 20, 2020
Camera-ready papers & Pre-Registration: October 1, 2020
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 CTM 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 2020 conference proceedings (to be published by IEEE).