Secure and Efficient Distributed Machine Learning in Cloud Computing

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: