Recent Advances on Federated Learning for Security and Privacy

Dear Colleagues,

Federated learning is a powerful machine learning technique that focuses on training models across heterogeneous decentralized devices, including IoT devices, mobile phones, and computers. This technique allows to bypass several drawbacks attributed to centralized machine learning. Federated learning allows ensuring advanced data privacy, while optimizing storage and processing requirements.

This Special Issue aims to present and disseminate the most recent advances related to federated machine learning and its application in finance, e-commerce, biometric, engineering, health, education and other relevant domains. We invite contributions addressing all theoretical, practical, and applied aspects of federated learning.   

Topics of interest for publication include, but are not limited to, the following: 

• Foundations of federated learning;
• Data leakage, robustness and heterogeneous data issues;
• Privacy and security aspects;
• Federated learning for network and asset security;
• Federated learning for biometric security;
• Federated learning in image and video processing;
• Federated learning for health data and medical devices;
• Federated learning for mobile computing;
• Federated learning and Internet of Things;
• Explainability and interpretability of federated learning;
• Trustworthiness and fairness of federated learning. 

Dr. Marina Gavrilova
Prof. Dr. Mariana Bento
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 250 words) can be sent to the Editorial Office for assessment.

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. Journal of Cybersecurity and Privacy 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 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.

• federated learning
• deep learning architecture
• heterogeneous data
• privacy
• cybersecurity
• biometric security
• health
• mobile computing
• image and video processing
• explainability
• trustworthiness
• fairness