Deep Learning Advances in Distributed Computing Environment
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: closed (1 October 2022) | Viewed by 814
Special Issue Editors
Interests: cloud computing; deep learning system and applications; distributed computing system
Special Issues, Collections and Topics in MDPI journals
Interests: computer vision; image processing; deep learning system and applications
Special Issue Information
Distributed learning is a promising paradigm for training deep networks in modern era. As the architecture of deep neural networks becomes bigger, many of vendors employ a centralized fashion of distributed learning with the parameter server in datacenters. Instead of centralizing data, recently popular federated learning is also one of the important branches to preserve data privacy; the server-free decentralized approaches are also considered to overcome data deficiency of each data silo. Meanwhile, since the reduction in convergence speed is the ultimate objective of distributed learning, the acceleration methodology is essentially required. Improving communication efficiency is also a significant topic. This would be more critical in limited resources, e.g., edge servers and mobile devices. In addition, combining with eXplainable AI (XAI) would become a future direction of distributed learning; this will lead to numerous upcoming applications.
This Special Issue aims to present the current state-of-the-art progress and trends in deep learning advances for distributed environments. Original theoretical and experimental studies in all aspects of distributed computing with regard to deep learning are welcome to this special issue.
Potential topics include but are not limited to:
- Distributed optimization methods for deep neural networks;
- Distributed deep learning in the datacenter environments;
- Privacy-preserving federated and distributed learning;
- Communication-efficient decentralized learning;
- Acceleration methods for distributed learning;
- Distributed learning on resource-constrained devices;
- Deep learning for resource management in distributed systems;
- XAI in distributed deep learning;
- Analysis or Applications of distributed learning;
- Distributed and decentralized learning in Cloud/Edge computing continue;
- Data-aware distributed and decentralized learning.
Dr. Patrizio Dazzi
Dr. Joon Huang Chuah
Dr. Heejae Kim
Guest Editors
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