2024 IEEE WCCI Special Session on “Privacy-Preserving Evolutionary Computation”
来源: 陈伟能/

Scope and Topics

In recent years, the rapid development of novel communication and distributed computing paradigms, such as cloud/edge computing, outsourcing computing, secure computing, and the Internet of Things (IoT), have brought new opportunities as well as new challenges to evolutionary computation (EC) methods. On the one hand, these new computing models can support distributed data perception, collection, learning, and optimization, promoting emerging research branches such as crowdsourcing, federated learning, and distributed data-driven optimization. EC has great potential in solving these distributed learning and optimization problems. On the other hand, the emerging computing paradigms also pose unprecedented privacy concerns for the EC frameworks, algorithms, and methods, rendered users severely vulnerable to privacy leakage, security attack, and fair decision. Hence, it is high time to investigate the privacy-preserving EC to cater for the era of cloud/edge computing, outsourcing computing, and federated computing.

This special session aims to bring together researchers, practitioners and developers from different background areas such as artificial intelligence, computational intelligence, data privacy, secure computing, cloud/edge computing, and crowdsourcing to discuss the latest experience, research ideas and synergic research and development on fundamental issues and applications about challenges including privacy, security, and fairness issues in EC.

The topics of this special session include but are not limited to the following topics:

  • Formulation of privacy, security, and fairness in evolutionary computation
  • New opportunities, challenges, and modeling brought by secure computing, outsourcing computing, federated computing to evolutionary computation
  •  Privacy-preserving, secure, or fairness-aware distributed optimization/learning/computing
  • Privacy-preserving, secure, or fairness-aware solutions for federated data-driven optimization/learning/computing
  • Secure computing powered privacy-preserving evolutionary computation
  • Evolutionary computation as a service
  • Privacy-preserving optimization paradigms for optimization, such as centralized optimization, distributed optimization, and surrogate-assistant optimization
  • Privacy-preserving and fairness-aware multi-objective optimization
  • Applications of privacy-preserving, secure, or fairness-aware framework, algorithms, and methods of evolutionary computation
  • Benchmarks/performance metrics for privacy-preserving, secure, or fairness-aware evolutionary computation


Important Dates

  • 15 November 2023: Special Session & Workshop Proposals Deadline
  • 15 December 2023: Competition & Tutorial Proposals Deadline
  • 15 January 2024: Paper Submission Deadline
  • 15 March 2024: Paper Acceptance Notification
  • 1 May 2024: Final Paper Submission & Early Registration Deadline
  • 30 June - 5 July 2024: IEEE WCCI 2024 Yokohama, Japan


Paper Submission

Papers submitted to this Special Session are reviewed according to the same rules as the submissions to the regular sessions of WCCI 2024. Authors who submit papers to this session are invited to mention it in the form during the submission. Submissions to regular and special sessions follow identical format, instructions, deadlines and procedures of the other papers.

For further information and news refer to the WCCI website: https://2024.ieeewcci.org/



Bowen Zhao received his Ph.D. degree in cyberspace security from South China University of Technology, China, in 2020. He was a research scientist at the School of Computing and Information Systems, Singapore Management University, Singapore from 2020 to 2021. Now, he is an associate professor at Guangzhou Institute of Technology, Xidian University, Guangzhou, China. His current research interests include privacy-preserving computation and learning and privacy-preserving MCS. He has published more than 30 papers on the topics of privacy protection of crowdsensing, secure computing including papers in IEEE TIFS, IEEE TMC, IEEE IoT Journal, and so on. He is a recipient of 2022 IEEE DSC Best Paper Award. He serves/has served as the TPC member for IEEE KSEM 2023, 2022, etc.

Wei-Neng Chen received the bachelor’s and Ph.D. degrees in computer science from Sun Yat-sen University, Guangzhou, China, in 2006 and 2012, respectively. Since 2016, he has been a Full Professor with the School of Computer Science and Engineering, South China University of Technology, Guangzhou. He has coauthored over 100 international journal and conference papers, including more than 70 papers published in the IEEE Transactions journals. His current research interests include computational intelligence, swarm intelligence, network science, and their applications. Dr. Chen was a recipient of the IEEE Computational Intelligence Society (CIS) Outstanding Dissertation Award in 2016, and the National Science Fund for Excellent Young Scholars in 2016. He was also a Principle Investigator (PI) of the National Science and Technology Innovation 2030, the Next Generation Artificial Intelligence Key Project. He is currently the Vice-Chair of the IEEE Guangzhou Section, and the Chair of IEEE SMC Society Guangzhou Chapter. He is also a Committee Member of the IEEE CIS Emerging Topics Task Force. He serves as an Associate Editor for the IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, and the Complex and Intelligent Systems.

Feng-Feng Wei received the bachelor’s degree in computer science and technology from the South China University of Technology, Guangzhou, China, in 2019, where she is currently pursuing the Ph.D. degree in computer science and technology with the School of Computer Science and Engineering. She is also a research assistant with the Guangdong-Hong Kong Big Data and Computational Intelligence Innovative Research Platform. Her current research interests include swarm intelligence, evolutionary computation, distributed optimization, edge–cloud computing, and their applications on expensive optimization in real-world problems.


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