Secure and Efficient Distributed Machine Learning Special Section

Special Section Overview

With the exponential growth of data generated by cloud-based services and edge devices, distributed machine learning has become a foundational pillar of modern AI applications. In particular, decentralized learning schemes—such as federated learning—have emerged as promising solutions that facilitate collaborative model training across multiple clients without requiring the exchange of raw data. This paradigm is particularly well-suited to cloud computing environments, where stringent privacy regulations, heterogeneous network conditions, and limited communication bandwidth pose significant challenges. Nonetheless, achieving robust security and maintaining high system efficiency in such distributed settings remain critical and unresolved issues.

Cloud infrastructures, despite offering substantial computational capabilities, are inherently characterized by resource asymmetry, dynamic connectivity, and diverse privacy constraints. Conventional distributed training frameworks, originally designed for centralized data centers with homogeneous hardware and stable networking, often prove inadequate in these heterogeneous and privacy-sensitive scenarios. Moreover, the distributed nature of decentralized learning introduces unique challenges, including Non-IID data distributions, client unreliability, asynchronous updates, and susceptibility to adversarial behaviors.

This special session aims to explore the convergence of secure and efficient distributed machine learning with cloud computing. We welcome original research contributions, forward-looking position papers, and preliminary investigations that address the theoretical foundations, algorithmic innovations, and practical deployments of secure, scalable, and efficient learning frameworks in distributed cloud environments.

Topics of interest include, but are not limited to:

Topics of Interest

Optimization Techniques for Distributed Learning in the Cloud (e.g., beyond first-order methods, adaptive and asynchronous optimization)
Communication- and Resource-Efficient Distributed Learning (e.g., communication compression, update sparsification, hardware co-design)
Handling System and Statistical Heterogeneity (e.g., data, model, and device heterogeneity; personalization strategies; cross-device and cross-silo settings)
Privacy-Preserving and Secure Learning Protocols (e.g., differential privacy, secure aggregation, robustness to adversarial attacks)
Incentive Mechanisms and Economic Models (e.g., game-theoretic designs, reputation systems, auctions, pricing)
Foundation Models in Federated/Distributed Contexts (e.g., parameter-efficient fine-tuning, knowledge distillation)
Responsible and Trustworthy Learning (e.g., fairness, bias, interpretability, accountability, ethical considerations)
Domain-Specific Applications (e.g., secure medical collaboration, financial forecasting with sensitive data, smart city and IoT analytics)

Important Dates

Paper Submission: September 6, 2025
Notification of Acceptance: October 4, 2025
Camera-Ready: October 30, 2025
Workshop Date: November 16, 2025 (TBC)

Submission Guidelines

Authors are invited to submit original, unpublished research papers. Submissions should be in IEEE format and not exceed 6 pages. All submissions will be peer-reviewed. Accepted papers will be published in the conference proceedings and indexed by IEEE Xplore.

Special Section Chairs

Xuehe Wang, Sun Yat-sen University, China
Xiaohua Jia, City University of Hong Kong, China
Jiannong Cao, The Hong Kong Polytechnic University, China
Jian Weng, Jinan University, China

Program Committee

Xiaofeng Chen, Xidian University, China (chenxf@xidian.edu.cn)
Jianfeng Ma, Xidian University, China (jfma@xidian.edu.cn)
Kaitai Liang, University of Surrey, UK (k.liang@surrey.ac.uk)
Jianbing Ni, Queen's University, Canada (jianbing.ni@queensu.ca)
Jian Shen, Nanjing University of Information Science and Technology, China (shenjian@nuist.edu.cn)

Contact Information

For any inquiries about the section, please contact:

Email: wangxuehe@mail.sysu.edu.cn

Submit Paper Download Special Section Flyer

Secure and Efficient Distributed Machine Learning in Cloud Computing