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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

Japanese

ランサムウェアによる攻撃は、重要なインフラをターゲットとする複雑な複数の段階のキャンペーンに発展し、年間数十億ドルの損害を引き起こす。従来の検出技術はゼロデーの変異に対して効果がなく、プライバシーの問題は集中型機械学習アルゴリズムによって提起されています。連合型学習は、データ共有なしで協力モデルトレーニングを可能にしますが、現在のFLアプローチは説明性の問題、通信コストの高さ、および非IIDデータの異質性に苦しんでいます。

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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