Journal of Federated and Distributed AI
"Advancing Privacy-Preserving and Decentralized Artificial Intelligence"
An international peer-reviewed Open Access journal focusing on key breakthroughs in collaborative machine learning models, system scalability, and privacy-preserving algorithms in decentralized ecosystems.
About the Journal
The Journal of Federated and Distributed AI (JFDAI) is a peer-reviewed, open access journal dedicated to publishing high-quality research in the rapidly evolving fields of federated learning, distributed AI systems, and privacy-preserving machine learning.
As artificial intelligence becomes increasingly pervasive, the need for decentralized, privacy-aware, and communication-efficient learning paradigms has never been greater. Traditional centralized AI approaches require aggregating vast amounts of sensitive data into single repositories, creating significant privacy risks, regulatory compliance challenges, and scalability bottlenecks.
JFDAI addresses this critical gap by providing a dedicated forum for researchers, practitioners, and industry professionals to share advancements in technologies that enable collaborative intelligence without centralizing data.
Journal Announcements
First Call for Papers: Open for Submissions (Volume 1, Issue 1)
June 4, 2026The Journal of Federated and Distributed AI (JFDAI) is officially launching and open for submissions. We invite original research articles, comprehensive review papers, and AI application articles. Benefit from fast desk reviews and developmental peer review feedback.
JFDAI Establishes Open Access Sponsorship Program
May 28, 2026To support early-career researchers, JFDAI is offering full APC fee waivers and discounts for its initial issues. Our goal is to encourage reproducible, sound science in decentralized learning ecosystems.