A Stackelberg Approach to Federated Learning for Malware Detection

Abstract

The widespread use of smart devices requires effective malware detection tools to ensure user security and privacy. The dynamic nature of the software ecosystem, characterized by data distribution changes, poses significant challenges to the long term sustainability of machine learning models for malware detection, requiring periodic updates to maintain their effectiveness. Additionally, collecting up-to-date information for training machine learning models in a centralized fashion is costly, time-consuming, and privacy-invasive. To address these shortcomings, this work proposes a Stackelberg game model to incentivize users to contribute to the training of a malware detection model through Federated Learning. The proposed model takes into account heterogeneous capabilities of the participants, allowing them to tune their contribution based on the quality and quantity of the data they can provide. Experimental results demonstrate that the proposed approach can ensure the effectiveness of the detection model over multiple years.