Iranian Society of Cryptology The ISC International Journal of Information Security 2008-2045 18 3 2026 07 01 HashLearner: A Secure Decentralized Learning Framework Based on HashGraph 191 205 242015 10.22042/isecure.2026.242015 EN Keyhan Mohammadi Department of Computer Engineering, Faculty of Engineering, University of Guilan, Rasht, Iran Ehasan Kozegar Department of Computer Engineering, Faculty of Technology and Engineering-East of Guilan, University of Guilan, Rudsar, Guilan, Iran Reza Ebrahimi Atani Department of Computer Engineering, Faculty of Engineering, University of Guilan, Rasht, Iran Journal Article Federated learning enables collaborative model training without centralized data collection, but existing frameworks rely on a central server, introducing risks of single points of failure, adversarial manipulation, and privacy leakage. To address these challenges, we propose HashLearner, a secure decentralized learning framework that utilizes the HashGraph consensus protocol for model aggregation without trusted authorities. HashLearner introduces two key innovations: (i) a consensus-driven decentralized aggregation mechanism resilient to Byzantine adversaries, and (ii) a privacy-preserving shuffling strategy that mitigates gradient reconstruction and poisoning attacks. To handle heterogeneous data distributions, the framework further employs transfer learning–based personalization. The simulation results of HashLearner, tested on benchmark Kaggle datasets, demonstrate that the platform maintains high accuracy while significantly enhancing scalability, security, and privacy. These findings indicate that HashLearner provides a practical path toward scalable, privacy-preserving, and trustworthy decentralized federated learning. https://www.isecure-journal.com/article_242015_0835915aab4a454be69eb0c2fd6c5600.pdf