Advances in Distributed Learning and Optimization over Networks: Techniques, Algorithms, and Applications
A special issue of IoT (ISSN 2624-831X).
Deadline for manuscript submissions: 30 September 2025 | Viewed by 167
Special Issue Editors
Interests: distributed learning; federated learning; distributed systems and security
Interests: AI-driven wireless networking and sensing systems
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Distributed learning and optimization over networks have become integral to modern applications such as wireless communication systems, machine learning, the Internet of Things (IoT), and large-scale data processing. With the rapid growth of data and the increasing need for efficient, scalable, and decentralized solutions, this field has seen remarkable advancements that continue to address emerging challenges and opportunities.
This Special Issue, titled “Advances in Distributed Learning and Optimization over Networks: Techniques, Algorithms, and Applications”, aims to provide a platform for researchers and practitioners to share cutting-edge methodologies, theoretical developments, and innovative applications. It addresses critical challenges such as scalability, convergence, robustness, and privacy in distributed systems, fostering advancements and bridging the gap between theoretical research and practical implementation.
Topics of interest include but are not limited to, distributed optimization methods, federated learning frameworks, multi-agent systems, and decentralized control mechanisms. Applications like smart grids, communication networks, and cloud computing are also highly relevant. Furthermore, submissions addressing practical aspects like hardware considerations, deployment strategies, and real-world case studies are encouraged to highlight these methods' practical impact.
This Special Issue welcomes high-quality contributions that align with its theme, whether they present original research or comprehensive reviews. We especially encourage submissions that provide novel insights, identify emerging trends, or adopt interdisciplinary approaches. We look forward to your valuable contributions to this dynamic and impactful field.
Prof. Dr. Gang Yan
Dr. Kang Yang
Dr. Guojun Xiong
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. IoT is an international peer-reviewed open access quarterly 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 1200 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.
Keywords
- distributed learning
- federated learning
- networking systems
- IoT applications
- cloud computing
- optimization algorithms
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