Efficient and Scalable Federated Learning for Real-World Applications

Dear Colleagues,

In domains where data cannot be centralized, Federated Learning (FL) is increasingly recognized as a key enabler for large-scale, privacy-aware machine learning. One example is healthcare. FL provides the foundation for building AI systems that are both secure and widely applicable by allowing institutions to collaborate without exchanging sensitive records. What ultimately slows the shift from research to practical deployment is a cluster of persistent challenges—challenges tied to computational efficiency, communication bottlenecks, scaling FL across large and diverse client networks, and its integration with existing infrastructures. Addressing these obstacles is essential to realizing the full potential of FL in environments, especially where timely and reliable performance is paramount.

This Special Issue will focus on advancing FL methods that are privacy-preserving—and also efficient, scalable, and deployable in real-world healthcare systems.

We welcome submissions that explore novel algorithmic designs for reducing communication and computation costs. Work that deals with the realities of scaling to heterogeneous and resource-constrained clients is also highly relevant. In addition, architectures that ensure robustness and fault tolerance in distributed environments are very much within scope. This Special Issue focuses on solutions that go beyond theoretical prototypes and move toward production-ready systems, as we hope to accelerate the deployment of FL in real-world applications. The ultimate goal is to advance AI technologies that are both technically scalable and clinically impactful, promoting safer and more equitable healthcare worldwide.

Dr. Bruno Casella
Guest Editor

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. Applied System Innovation 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 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.

• federated learning
• critical applications
• real-world applications
• distributed learning
• healthcare
• medical imaging
• privacy-preserving artificial intelligence
• AI for financial applications
• decentralized learning