议题:
Data are the oil for the operation of artificial intelligence. Nevertheless, with the exception of a few industries, the
data available in most fields are of limited quantity or poor quality, making it hard to realize effective artificial
intelligence applications. Due to various data policy restrictions, it is very difficult in many situations to break the
barriers between data sources, and thus almost impossible to integrate the data scattered around different institutions,
thus data always exist in the form of isolated islands. Therefore, it has been increasingly challenging to build efficient
joint models while meeting privacy, security, and regulatory requirements, especially with scattered data or limited
computational resources. In this context, there is a rising interest in multi-party learning, where data owners train
machine learning models collaboratively without sharing their private data, whereas can benefit from the data of
others. This is a privacy-preserving approach that enables collaborative learning by training on individual data and
sharing knowledge of their local models, which achieves privacy preservation while leveraging distributed
computational resources efficiently. In recent years, multi-party collaborative learning has been shown to be the
premier method for solving data silo problems, producing both secure and flexible solutions compared with
centralized training. This is particularly true for scattered limited, and private data settings in computational
intelligence, which is the case in real-world collaborative learning and optimization problems with many participants.
Multi-party learning can be used to address machine learning challenges under diverse distributed data settings. In
addition to the privacy issues, multi-party learning has great advantages in leveraging the computational resources of
massive edge devices, and it achieves superior performance in building customized and personalized models
according to local individual data. However, existing multi-party learning has its restrictions in the face of increasing
multi-source data, and there are new challenges in overcoming the limitations imposed by designing new optimization
strategies, privacy and security mechanisms, and concrete implementations in complex data environments.
This special session aims to showcase the importance of multi-party collaborative learning in computational
intelligence. The guest editors believe that there is great potential in using multi-party learning to address machine
learning challenges such as privacy and security concerns, distributed settings, and collaborative optimizations. The
special session promotes developing new models or integrated frameworks in multi-party collaborative learning,
improving the efficiency of building joint models, and introducing novel research areas. It also encourages
implementing developed frameworks to solve real-world problems in different application areas such as healthcare,
finance, and advertising. Special attention will be devoted to addressing challenges in multi-party learning,
optimizing communication-accuracy trade-off, developing general multi-party learning, and using collaborative
decision-making in prediction and pattern recognition. The goal of this special session is to solicit high-quality,
original research contributions on advances in theory, algorithms, systems, and applications of multi-party
collaborative learning, thereby capturing the state of the art and stimulating further developments in the related areas.
会议主题:
The main topics of this special session include, but are not limited to, the following:
Aggregation scheme in multi-party learning
Incentives mechanism in multi-party learning
Fairness and interpretability in multi-party learning
Asynchronous Communication in multi-party learning
Privacy, security, and robustness in multi-party learning
Fault tolerance and active sampling in multi-party learning
Adaptive and personalized techniques in multi-party learning
Resource allocation and management in multi-party learning
Heterogeneous data, model, and system in multi-party learning
Model compression and communication efficiency in multi-party learning
Collaborative optimization and convergence analysis in multi-party learning
Incomplete, imbalance, and multimodal data in multi-party learning
New applications for collaborative multi-party learning
组织者简介:
高原高远于2018年和2022年分别获得西安电子科技大学的工学学士学位和博士学位。自2023年以来，他一直是西安电子科技大学电子工程学院的博士后研究员。他于2021年至2022年期间，曾作为访问学者在新加坡南洋理工大学的计算机科学与工程学院进行学术访问。他是中国博士后科学基金支持计划的获奖者。他目前的研究兴趣包括安全人工智能、多方学习和协同优化。到目前为止，他已在上述领域的多个高质量和有影响力的期刊上发表或合作发表了众多学术论文，例如IEEE T-PAMI、T-CYB、T-NNLS、T-KDE等。此外，他还担任多个领先领域的期刊和会议的审稿人，以及国际会议的程序委员会成员。
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