ICMLA, Advanced Machine Learning and Applications: Federated Learning and Meta-Learning
AML-IoT FLAME 2020
- URL: https://sites.google.com/view/aml-iot-flame/about
- Event Date: 2020-12-14 ~ 2020-12-17
- Submission Date: 2020-08-30
- Location: Miami, United States of America
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
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
Journal Promotion
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| Acta Koreana (Acta Koreana) |
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| Acta Via Serica (AVS) |
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| Computers and Concrete (CAC) |
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| Journal of Information Processing Systems (JIPS) |
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| Journal of Internet Computing and Services (KSII - JICS) |
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| Journal of Smart Tourism (Smart Tourism) |
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| Membrane and Water Treatment (MWT) |
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| Wind and Structures, An International Journal (Wind and Structures) |
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