New Insights into Multimodal Learning and Federated Learning

Dear Colleagues,

In the rapidly evolving landscape of artificial intelligence, multimodal learning and federated learning are transforming data processing and model development. By integrating diverse data sources such as text, images, audio, and video, multimodal learning, enables machines to understand and interpret the world in a more human-like manner, thereby enhancing the performance and versatility of intelligent systems. On the other hand, federated learning allows multiple parties to collaboratively train machine learning models without sharing their raw data, addressing critical concerns related to data privacy, security, and regulatory compliance. These technologies have significant implications across various sectors, including healthcare, finance, education, and smart cities, where the ability to handle complex data and ensure data protection is crucial.

We would like to invite you to contribute to our upcoming Special Issue, “New Insights into Multimodal Learning and Federated Learning”, in Electronics. It aims to provides a platform for researchers, practitioners, and industry experts to share their latest findings, innovative ideas, and practical experiences in this research area.​

This Special Issue aims to explore the state-of-the-art advancements, emerging trends, and future directions in multimodal learning and federated learning. We welcome a wide range of article types, including original research papers, review articles, and case studies, that offer novel perspectives and contributions to this field.​

Research areas may include (but are not limited to) the following:​

• Advanced algorithms for multimodal data fusion in federated learning environments;​
• Techniques for enhancing privacy preservation in multimodal federated learning;​
• Applications of multimodal learning in real-world scenarios such as healthcare diagnosis and autonomous driving;​
• Challenges and solutions in model aggregation for multimodal federated learning;​
• The integration of transfer learning with multimodal federated learning frameworks.

We look forward to receiving your contributions.

Dr. Yutao Yue
Dr. Runwei Guan
Dr. Daizong Liu
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. Electronics 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.

• multi-modal learning
• federated learning
• data fusion
• transfer learning
• real-world applications
• healthcare diagnosis
• autonomous driving
• machine learning frameworks