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Malware detection has become increasingly challenging due to the evolving complexity and diversity of malicious software. Federated learning (FL) provides a promising framework for collaborative model training without sharing sensitive data, making it particularly suitable for malware detection across distributed and heterogeneous data sources. This paper proposes an FL-based malware detection approach that addresses the challenges posed by data heterogeneity. By converting binary malware samples into grayscale images and leveraging convolutional neural networks (CNN), our method achieves effective feature extraction while ensuring cross-platform consistency. To mitigate the impact of non-independent and identically distributed (non-IID) data, we introduce an improved loss function that incorporates a regularization term to align local and global models. Experimental results on the Big2015 dataset demonstrate that the proposed approach outperforms traditional FL methods, such as FedAvg, in scenarios with heterogeneous data distributions. Our findings highlight the effectiveness of combining FL with image-based malware detection techniques in addressing real-world challenges in distributed environments.

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A Federated Learning Approach for Malware Detection in Data Heterogeneous Environments

  • 会议论文

  • 发表日期：

    2026年

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  • Haoyuan Wen,
  • Jingfeng Xue,
  • Wenjie Guo,
  • Liuting Wang,
  • Wenbiao Du

摘要

Malware detection has become increasingly challenging due to the evolving complexity and diversity of malicious software. Federated learning (FL) provides a promising framework for collaborative model training without sharing sensitive data, making it particularly suitable for malware detection across distributed and heterogeneous data sources. This paper proposes an FL-based malware detection approach that addresses the challenges posed by data heterogeneity. By converting binary malware samples into grayscale images and leveraging convolutional neural networks (CNN), our method achieves effective feature extraction while ensuring cross-platform consistency. To mitigate the impact of non-independent and identically distributed (non-IID) data, we introduce an improved loss function that incorporates a regularization term to align local and global models. Experimental results on the Big2015 dataset demonstrate that the proposed approach outperforms traditional FL methods, such as FedAvg, in scenarios with heterogeneous data distributions. Our findings highlight the effectiveness of combining FL with image-based malware detection techniques in addressing real-world challenges in distributed environments.