Deep Learning in Parallel and Distributed Data Applications and Systems

Dear Colleagues,

Deep learning (DL) has become an increasingly important area of research in recent years and has become a critical tool for addressing complex analysis tasks in various data applications, particularly in fields such as computer vision (CV), natural language processing (NLP), and data analytics. This is largely due to the advancement of highly parallel and distributed computing systems, which can support the intensive computations required by deep learning algorithms. However, how to perform model training and inference in an efficient way, e.g., for large models and large datasets, is still challenging with the current techniques. On the other hand, with the increasing complexity of computing paradigms, such as IoT, edge, and cloud computing, deep learning techniques such as deep reinformance learning have been used to optimize the mangement of parallel and distributed data applications and systems. Regardless, as these applications and systems grow in size and complexity, current DL-aided solutions still encounter problems in ensuring the security, privacy, and scalability of parallel and distributed data applications and systems

In this Special Issue, we seek to explore the latest advances and challenges in the context of deep learning in parallel and distributed data applications and systems. Specifically, we are interested in original research that explores novel algorithms, architectures, systems and applications for deep learning in parallel and distributed settings, as well as papers that address the challenges and limitations of the existing approaches. Topics to be covered in this Special Issue might include, but are not limited to, the following:

• The development of new parallel and distributed architectures for DL;
• Performance optimization for DL using large-scale datasets;
• Theoretical foundations of parallel and distributed DL;
• Challenges and limitations of existing approaches to parallel and distributed deep learning;
• The use of DL in the optimization of parallel and distributed data applications and systems;
• The integration of DL with IoT, edge, and cloud computing paradigms;
• DL for security and privacy protection in distributed and parallel data applications and systems;
• DL for parallel and distributed applications in CV and NLP;
• The application of DL to big data applications and systems;
• Novel applications of DL in parallel and distributed enviroments.

We invite submissions of high-quality, original research papers, as well as review articles that provide a comprehensive overview of the state of the art in the fields of deep learning and parallel and distributed computing. We also welcome papers that present case studies and real-world applications of deep learning in parallel and distributed data applications and systems.

This is a unique opportunity for researchers to share their latest findings and ideas with a broad audience, and to help advance our understanding of the interactions between deep learning and parallel and distributed computing. We look forward to receiving your submissions.

Dr. Long Cheng
Guest Editor

Manuscript Submission Information

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• deep learning
• parallel computing
• distributed systems
• big data