Journal Menu► ▼ Journal Menu
Journal Browser► ▼ Journal Browser
Special Issue "Federated and Transfer Learning Applications"
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 (20 March 2023) | Viewed by 9125
Special Issue Editors
Interests: privacy-enhancing technologies (PETs); information security; distributed ledger technologies (DLTs); biomedical informatics; federated learning; transfer learning
Special Issues, Collections and Topics in MDPI journals
Interests: algorithms; social network analysis; federated learning; algorithmic aspects of privacy; algorithmic game theory
Interests: information retrieval; databases; data science; machine learning
Special Issue Information
The classic example of machine learning is based on isolated learning—a single model for each task using a single dataset. Most deep learning methods require a significant amount of labeled data, preventing their applicability in many areas where there is a shortage. In these cases, the ability of models to leverage information from unlabeled data or data that is not publicly available (for privacy and security reasons) can offer a remarkable alternative. Transfer learning and federated learning are such alternative approaches that have emerged in recent years. More precisely, transfer learning is defined as the set of methods that leverage data from additional fields or tasks to train a model with greater generalizability and usually use a smaller amount of labeled data (via fine-tuning) to make them more specific for dedicated tasks. Accordingly, federated learning is a learning model that seeks to address the problem of data management and privacy through joint training with this data, without the need to transfer the data to a central entity.
In this Special Issue, we seek research and case studies that demonstrate the application of federated and transfer learning approaches to support applied scientific research, in any area of science and technology. Example topics include (but are not limited to) the following:
- Federated Learning (FL) Applications.
- Distributed Learning Approaches.
- Privacy-Preserving Techniques in FL.
- Homomorphic Encryption Approaches in FL.
- Differential Privacy Approaches in FL.
- Incentive Mechanisms in FL.
- Interpretability in FL.
- FL with Unbalanced Data.
- Selection of Appropriate FL Aggregation Function per Application.
- Transfer Learning (TL) Applications.
- Pre-Trained Models.
- BERT-Like Models.
- Federated Transfer Learning Approaches.
- Applications of FL and TL in Biomedical Domain.
- Applications of FL and TL in Cybersecurity.
- Applications of FL and TL in Natural Language Processing.
- Applications of FL and TL in Social Network Analysis.
- Graph-based FL and TL.
Dr. George Drosatos
Prof. Dr. Pavlos S. Efraimidis
Prof. Dr. Avi Arampatzis
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 2300 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.
- federated learning
- transfer learning
- artificial intelligence
- pre-trained models
- BERT-like modes
- distributed learning
- homomorphic encryption
- differential privacy