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Advances in Mathematical Methods for Distributed Learning and High-Dimensional Data...
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Advances in Mathematical Methods for Distributed Learning and High-Dimensional Data Analysis

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Probability and Statistics".

Deadline for manuscript submissions: 31 December 2024 | Viewed by 256

Special Issue Editors

Dr. Keren Li

E-Mail Website
Guest Editor
Department of Mathematics, University of Alabama at Birmingham, Birmingham, AL, USA
Interests: statistical learning; distributed learning; federated learning; deep learning; generalized linear models; graphical models; variable selection methods
Prof. Dr. Ji-Ping Wang

E-Mail Website
Guest Editor
1. Department of Statistics, Northwestern University, Evanston, IL 60208, USA
2. NSF-Simons Center for Quantitative Biology, Northwestern University, Evanston, IL 60208, USA
Interests: statistical applications in bioinformatics; computational biology
Dr. Baocheng Geng

E-Mail Website
Guest Editor
Department of Computer Science, University of Alabama at Birmingham, Birmingham, AL, USA
Interests: human decision making; distributed detection and information fusion; optimization for signal processing and machine learning; Internet of Things

Special Issue Information

Dear Colleagues,

We are pleased to invite contributions to our upcoming Special Issue of Mathematics, entitled "Advances in Mathematical Methods for Distributed Learning and High-Dimensional Data Analysis". This Issue seeks to delve into the intricate intersection of distributed learning and high-dimensional data analysis, which presents both unique challenges and groundbreaking opportunities at the vanguard of mathematical research. The importance of this research area lies in its potential to unlock new understandings and applications in fields ranging from computational biology to artificial intelligence, reflecting the evolving complexity of data and computation in the modern world.

This Special Issue aims to address both the theoretical underpinnings of this intersection and its practical implementations, while concurrently advancing the journal’s commitment to advancing significant mathematical breakthroughs. Our goal is to collate research that showcases innovative mathematical models, algorithms, and applications pertinent to distributed learning and high-dimensional data analysis. If successful, this collection will offer a comprehensive perspective on the mathematical challenges and solutions in these intertwined fields, with potential to be published in book form.

In this Special Issue, we welcome original research articles and reviews that cover a range of topics within our scope. Potential themes for submissions include, but are not limited to:

  • Theoretical and practical challenges in merging distributed learning with high-dimensional data.
  • Development of innovative mathematical models and algorithms for distributed environments and complex data structures.
  • Exploration of deep learning techniques within the context of distributed learning and high-dimensional data.
  • Empirical case studies demonstrating the real-world application of these theoretical concepts.

We look forward to receiving your contributions.

Dr. Keren Li
Prof. Dr. Ji-Ping Wang
Dr. Baocheng Geng
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. Mathematics 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 2600 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
  • high-dimensional data inference
  • high-dimensional data analysis
  • variable selection
  • federated learning
  • online learning
  • deep learning
  • data privacy
  • big data analytics
  • complex data structures

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Further information on MDPI's Special Issue polices can be found here.

Published Papers

This special issue is now open for submission, see below for planned papers.

Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: 
"Representative Learning: An Innovative Distributed Learning Framework"

Abstract: 
"In the era of imminent superintelligence, the development of AI systems that are both powerful and aligned with human oversight is critical. This paper presents Representative Learning, a novel distributed machine learning framework, as a solution to enhance the safety and interpretability of superhuman AI systems. By generating pseudo data points or 'representatives' that encapsulate essential data features within local nodes, this approach facilitates efficient, privacy-conscious analysis without direct data transfer, addressing communication inefficiency, data heterogeneity, and security concerns of traditional Federated Learning methods. Representative Learning significantly reduces communication overhead, enhances scalability and adaptability, and improves interpretability in both centralized and decentralized heterogeneous environments. Representative Learning's core innovation enhances distributed networks by enabling task-specific model diversity, significantly boosting efficiency, adaptability, and interpretability across various architectures while ensuring data privacy and communication efficacy. The paper explores theoretical underpinnings, practical applications in high-stakes domains, and introduces the Anchored Score-Matching Representative (ASMR) technique, underscoring its potential to ensure superintelligent AI systems remain controllable, transparent, and aligned with human intentions and values."
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