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Open Access

ARTICLE

FedDPL: Federated Dynamic Prototype Learning for Privacy-Preserving Malware Analysis across Heterogeneous Clients

Danping Niu1, Yuan Ping1,*, Chun Guo2, Xiaojun Wang3, Bin Hao4
1 School of Information Engineering, Xuchang University, Xuchang, 461000, China
2 College of Computer Science and Technology, Guizhou University, Guiyang, 550025, China
3 School of Electronic Engineering, Dublin City University, Dublin, D09 V209, Ireland
4 Here Data Technology, Shenzhen, 518000, China
* Corresponding Author: Yuan Ping. Email: email

Computers, Materials & Continua https://doi.org/10.32604/cmc.2025.073630

Received 22 September 2025; Accepted 12 November 2025; Published online 03 December 2025

Abstract

With the increasing complexity of malware attack techniques, traditional detection methods face significant challenges, such as privacy preservation, data heterogeneity, and lacking category information. To address these issues, we propose Federated Dynamic Prototype Learning (FedDPL) for malware classification by integrating Federated Learning with a specifically designed K-means. Under the Federated Learning framework, model training occurs locally without data sharing, effectively protecting user data privacy and preventing the leakage of sensitive information. Furthermore, to tackle the challenges of data heterogeneity and the lack of category information, FedDPL introduces a dynamic prototype learning mechanism, which adaptively adjusts the clustering prototypes in terms of position and number. Thus, the dependency on predefined category numbers in typical K-means and its variants can be significantly reduced, resulting in improved clustering performance. Theoretically, it provides a more accurate detection of malicious behavior. Experimental results confirm that FedDPL excels in handling malware classification tasks, demonstrating superior accuracy, robustness, and privacy protection.

Keywords

Malware classification; data heterogeneity; federated learning; clustering; differential privacy

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