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 +1

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 learning system for ransomware detection. Our approach includes progressive XGBoost complexity to avoid overfitting, a decaying learning rate schedule for stable convergence, and dynamic client grading based on ransomware prevalence and data sufficiency. Using 62,485 real-world ransomware samples from four OS-based non-IID clients, we compare AdaFedXplain against FedAvg, FedProx, and SCAFFOLD. According to experimental data, AdaFedXplain outperforms all baselines with 99.59% detection accuracy and 99.64% F1-score while cutting energy usage by 75%, latency by 65%, and communication overhead by…