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AdaFedXplain: Adaptive Federated Learning with Explainable AI for Ransomware Detection

Md. Emran Biswas, Md. Delowar Hossain, Mst. Tanbin Yasmin Tanny, Muhammad Aminur Rahaman, Md Al Amin, Tangina Sultana · 2026 · arXiv

DOI: 10.48550/arXiv.2610.25318 · open-access PDF

Indonesian

Serangan menggunakan ransomware telah berkembang menjadi kampanye multifas yang kompleks yang menargetkan infrastruktur penting, mengakibatkan kerusakan tahunan miliaran dolar. Teknik deteksi konvensional tidak efektif terhadap variasi zero-day, dan masalah privasi dibangkitkan oleh algoritma pembelajaran mesin terpusat. Federated learning memungkinkan pelatihan model kolaboratif tanpa berbagi data, namun pendekatan FL saat ini menderita masalah penjelasan, biaya komunikasi tinggi, dan heterogenitas data non-IID.

Machine translation (Meta M2M100); the English original follows.

Abstract

Attacks using ransomware have developed into complex multiphase campaigns that target vital infrastructure, resulting in annual damages of billions of dollars. Conventional detection techniques are ineffective against zero-day variations, and privacy issues are raised by centralized machine learning algorithms. Federated learning allows for cooperative model training without data sharing, however current FL approaches suffer from explainability issues, high communication cost, and non-IID data heterogeneity. In this study, we present AdaFedXplain, an explainable AI-based adaptive federated lea

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