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Article

Research into Robust Federated Learning Methods Driven by Heterogeneity Awareness

1
School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China
2
School of Mathematics and Information Technology, Hebei Normal University of Science & Technology, Qinhuangdao 066004, China
*
Author to whom correspondence should be addressed.
Appl. Sci. 2025, 15(14), 7843; https://doi.org/10.3390/app15147843 (registering DOI)
Submission received: 16 June 2025 / Revised: 10 July 2025 / Accepted: 11 July 2025 / Published: 13 July 2025
(This article belongs to the Special Issue Cyber-Physical Systems Security: Challenges and Approaches)

Abstract

Federated learning (FL) has emerged as a prominent distributed machine learning paradigm that facilitates collaborative model training across multiple clients while ensuring data privacy. Despite its growing adoption in practical applications, performance degradation caused by data heterogeneity—commonly referred to as the non-independent and identically distributed (non-IID) nature of client data—remains a fundamental challenge. To mitigate this issue, a heterogeneity-aware and robust FL framework is proposed to enhance model generalization and stability under non-IID conditions. The proposed approach introduces two key innovations. First, a heterogeneity quantification mechanism is designed based on statistical feature distributions, enabling the effective measurement of inter-client data discrepancies. This metric is further employed to guide the model aggregation process through a heterogeneity-aware weighted strategy. Second, a multi-loss optimization scheme is formulated, integrating classification loss, heterogeneity loss, feature center alignment, and L2 regularization for improved robustness against distributional shifts during local training. Comprehensive experiments are conducted on four benchmark datasets, including CIFAR-10, SVHN, MNIST, and NotMNIST under Dirichlet-based heterogeneity settings (alpha = 0.1 and alpha = 0.5). The results demonstrate that the proposed method consistently outperforms baseline approaches such as FedAvg, FedProx, FedSAM, and FedMOON. Notably, an accuracy improvement of approximately 4.19% over FedSAM is observed on CIFAR-10 (alpha = 0.5), and a 1.82% gain over FedMOON on SVHN (alpha = 0.1), along with stable enhancements on MNIST and NotMNIST. Furthermore, ablation studies confirm the contribution and necessity of each component in addressing data heterogeneity.
Keywords: federated learning; data heterogeneity; heterogeneity-aware; weighted aggregation; multi-loss function federated learning; data heterogeneity; heterogeneity-aware; weighted aggregation; multi-loss function

Share and Cite

MDPI and ACS Style

Song, J.; Zheng, Z.; Li, A.; Xia, Z.; Liu, Y. Research into Robust Federated Learning Methods Driven by Heterogeneity Awareness. Appl. Sci. 2025, 15, 7843. https://doi.org/10.3390/app15147843

AMA Style

Song J, Zheng Z, Li A, Xia Z, Liu Y. Research into Robust Federated Learning Methods Driven by Heterogeneity Awareness. Applied Sciences. 2025; 15(14):7843. https://doi.org/10.3390/app15147843

Chicago/Turabian Style

Song, Junhui, Zhangqi Zheng, Afei Li, Zhixin Xia, and Yongshan Liu. 2025. "Research into Robust Federated Learning Methods Driven by Heterogeneity Awareness" Applied Sciences 15, no. 14: 7843. https://doi.org/10.3390/app15147843

APA Style

Song, J., Zheng, Z., Li, A., Xia, Z., & Liu, Y. (2025). Research into Robust Federated Learning Methods Driven by Heterogeneity Awareness. Applied Sciences, 15(14), 7843. https://doi.org/10.3390/app15147843

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