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 813
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
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Benefits of Publishing in a Special Issue
- Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
- Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
- Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
- External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
- e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.
Further information on MDPI's Special Issue policies can be found here.