TRUSTWORTHY MACHINE LEARNING
As part of 23rd IEEE International Conference on Data Mining (IEEE ICDM 2023) - CORE A*
December 1st - December 4th, 2023 @ ICDM 2023 in Shanghai, China

INTRODUCTION
The widely deployed machine learning models and algorithms in the inter-connected intelligent systems, such as IoT systems, have demonstrated a high level of potential for human daily life. Emerging as a novel technique to support the intelligent systems for a wide range of societal activities, such as autonomous unmanned aerial vehicles network, transportation systems and health care applications, trustworthy machine learning (TML) has become a focus for worldwide researchers. TML is designed as a pivotal and technical solution to ensure the various learning algorithms behave in a socially responsible manner and meet certain compliance requirements from the government and international organisations. One main goal is to investigate the different principles and constraints for TML-SPPIoT to be applied in IoT systems for a security and privacy preserving goal by a broad spectrum of researchers and practitioners. This workshop will focus on discussing the theories, principles, and experiences of developing trustworthy machine learning algorithms and models for such intelligent IoT systems, considering the mass inter-connected devices such as UAV, edge sensors and so on. This workshop will be the first attempt of gathering researchers interested in the emerging and interdisciplinary filed of trustworthy machine learning from its technical perspective and bringing the impacts in intelligent IoT systems for a goal of security and privacy. This workshop will highlight the recent related works and foster unprecedented chance to bridge the research gaps across the topics of deep learning, machine learning, IoT, security, fairness, privacy and so on. This workshop will conduct a reflection on foundations (theory and application) of trustworthy machine learning and lay out a positive vision for future collaboration and research activities for intelligent IoT systems.

SUBMISSION
Paper submissions should be limited to a maximum of 8 pages, and follow the IEEE ICDM format. More detailed information is available in the IEEE ICDM 2023 Submission Guidelines.

All the papers should be submitted following the official ICDM website: TML-SPPIoT Submission Portal.

All accepted papers will be included in the ICDM'23 Workshop Proceedings (ICDMW 2023) published by the IEEE Computer Society Press. Therefore, papers must not have been accepted for publication elsewhere or be under review for another workshop, conferences or journals.

All accepted papers, including workshops, must have at least one “FULL” registration. A full registration is either a “member” or “non-member” registration. Student registrations are not considered full registrations. All authors are required to register by 15th October 2023.

The topic should be related to trustworthy machine learning, including but not limited to:

Theoretical understanding of trustworthy machine learning, such as trustworthy graph learning, trustworthy federated learning and so on
Innovative methods for building trustworthy machine learning
Explainable and interpretable machine learning
Privacy-preserving machine learning
New applications of trustworthy machine learning in intelligent systems
Innovative machine learning models to build trustworthiness
Futuristic concerns of trustworthy machine learning

TENTATIVE IMPORTANT DATES
**All times are at 11:59PM Beijing Time**
Paper submission deadline: September 15th, 2023
Notification to Authors: September 24st, 2023
Camera-ready Deadline: October 1st, 2023
Registration: October 15th
Conference date: December 1st - 4th, 2023
WORKSHOP PROGRAM
To be updated

INVITED SPEAKERS
To be updated

Organizing Committee:

Jun Shen
University of Wollongong

Jianming Yong
University of Southern Queensland

Amir H. Gandomi
University of Technology Sydney

Fang Dong
Southeast University

Yuefeng Li
Queensland University of Technology

Jiuyong Li
University of South Australia

Yuxiang Wang
Hangzhou Dianzi University

Minhui Xue
CSIRO's Data61

Xiaoyu Xia
RMIT

Huaming Chen
The University of Sydney


Volunteers / Student Organizers:
Akbar Telikani
University of Wollongong


CONTACT US
Point of Contact:
Huaming Chen
The University of Sydney
Email: huaming.chen@sydney.edu.au