AdaFedXplain: Adaptive Federated Learning with Explainable AI for Ransomware Detection

Privacy protection through collaborative learning: AdaFedXplain enables organizations to train ransomware detection models together without sharing sensitive data, addressing a critical privacy concern with traditional centralized machine learning approaches that require pooling data from multiple organizations.

Explainability for security professionals: The system uses SHAP (SHapley Additive exPlanations) to identify which specific features in executable files influence detection decisions, allowing security analysts to understand why the system flagged something as ransomware rather than treating it as a "black box"—crucial for building trust in AI-driven security tools.

Significant efficiency gains: AdaFedXplain reduces energy consumption by 75%, communication overhead by 75%, and latency by 65% compared to existing federated learning approaches, making collaborative AI more practical for resource-constrained security operations while maintaining 99.59% detection accuracy.

Handles real-world data challenges: The system was tested on 62,485 real ransomware samples across four different operating systems with non-uniform data distributions, demonstrating it can work with the messy, imbalanced data that organizations actually encounter rather than idealized datasets.

Adaptive approach to heterogeneous environments: By dynamically grading participating clients based on ransomware prevalence and data quality in their environment, the system accounts for the reality that different organizations face different threat landscapes and have different data characteristics.