Guest Editors
Assoc. Prof. Qingguo Lü
Email: qglv@cqu.edu.cn
Affiliation: School of Computer Science, Chongqing University, Chongqing, China
Homepage:
Research Interests: distributed optimization, privacy protection, machine learning, smart grid
Dr. Dong Li
Email: lilvmy@163.com
Affiliation: College of Computer, Chongqing University, Chongqing, China
Homepage:
Research Interests: searchable encryption, privacy-preserving machine learning, cryptography, distributed optimization
Prof. Huaqing Li
Email: huaqingli@swu.edu.cn
Affiliation: College of Electronic and Information Engineering, Southwest University, Chongqing, China
Homepage:
Research Interests: optimization control, machine learning, artificial learning, distributed algorithm, multi-agent systems
Prof. Xiaofeng Liao
Email: xfliao2025@126.com
Affiliation: College of Computer, Chongqing University, Chongqing, China
Homepage:
Research Interests: computational intelligence and information security, privacy protection, artificial neural networks, cryptography, and its applications
Summary
The rapid advancement of digital technologies such as cloud computing, the Internet of Things (IoT), edge computing, and cyber–physical systems has led to a dramatic increase in distributed data generation and large-scale collaborative computing. Modern intelligent applications increasingly rely on distributed machine learning to train models across geographically distributed devices and data sources. While this paradigm enables scalable learning and efficient utilization of distributed resources, it also raises significant challenges related to optimization efficiency, communication overhead, system security, and data privacy. Ensuring robust optimization and secure collaboration in distributed learning environments has therefore become a critical research problem for building reliable and trustworthy intelligent systems.
This Special Issue aims to provide a platform for researchers and practitioners to present recent advances in the optimization methods and security mechanisms of distributed machine learning systems. The issue focuses on novel optimization algorithms, communication-efficient learning strategies, privacy-preserving training techniques, and secure distributed system architectures that enhance the scalability, reliability, and robustness of distributed learning frameworks. Interdisciplinary contributions that integrate optimization theory, machine learning, cybersecurity, and distributed computing are particularly encouraged. Both theoretical studies and practical implementations—including analytical models, algorithmic innovations, system frameworks, and real-world application demonstrations—are welcome to advance the development of efficient, secure, and trustworthy distributed machine learning systems.
Keywords
machine learning; control systems; information security; mathematical optimization; complex networks; neural networks; modelling and simulation; multi-agent systems; distributed optimization; federated learning
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