Advancements in Personalized Learning for Decentralized and Federated Environments

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: 30 November 2024 | Viewed by 109

Special Issue Editor

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Guest Editor
Department of Physics, University of Patras, 26504 Rion, Greece
Interests: patter recognition; computer vision; machine learning; feature and representation learning; learning from sequences; activity recognition
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recent advancements in Machine Learning have led to the establishment of foundational models for vision, audio and language. These models are trained and deployed in a centralized manner, requiring vast amounts of storage and computer power as well as bandwidth. Edge devices offer an attractive alternative for both the training as well as for the inference of these large models. Federated and decentralized learning and personalized learning unlock the potential for bringing smaller and more efficient models to the edge. Additionally, personalized learning can enable the deployment of models designed to run on high-end platforms on small and resource-restricted platforms that are only able to support tinyML models.

This Special Issue is dedicated to exploring methods, tools, and algorithms that focus on Federated and Decentralized approaches for personalized learning in the realms of vision, audio, and language, emphasizing the deployment and utilization of Edge devices.

Dr. Dimitris Kastaniotis
Guest Editor

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 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.


  • personalized learning
  • federated learning
  • split learning
  • decentralized learning
  • personalized learning for Edge and TinyML devices

Published Papers

This special issue is now open for submission.
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