# Research into Robust Federated Learning Methods Driven by Heterogeneity Awareness
## 5 Conclusions  
To address the pervasive non-IID data distribution problem in federated learning, this paper proposes a heterogeneity-aware robust federated learning method. The method introduces heterogeneity quantification and perception mechanisms at both the client train ing and server aggregation stages to enhance the generalization ability and convergence stability of the global model. Specifically, during local training on clients, a composite loss function is designed, incorporating classification loss, heterogeneity loss, L2 regularization, and feature center constraint, enabling structured optimization of the learning process. On the server side, a heterogeneity score is computed based on the clients' feature distributions, and a corresponding weighted aggregation strategy is employed to effectively reduce the impact of highly heterogeneous clients on the global model. The proposed method is extensively validated on CIFAR-10, SVHN, MNIST, and NotMNIST under both mild (\(\alpha\) = 0.5) and severe (\(\alpha\) = 0.1) heterogeneity settings. Compared to existing classical methods, the proposed approach consistently achieves a superior performance across different datasets and heterogeneity levels. Additionally, ablation experiments confirm the critical roles of the heterogeneity loss, feature center constraint, and weighted aggregation in the overall framework.  
These findings demonstrate that the proposed method offers significant improvements in robustness, generalization, and adaptability, outperforming existing mainstream approaches across a range of tasks and heterogeneity scenarios. The proposed framework provides both theoretical insight and practical guidance for building robust federated learning systems in real-world non-IID environments.  
Future research directions could focus on several promising areas. First, the integration of more advanced heterogeneity metrics and optimization techniques could be explored to further enhance the convergence speed and model performance, particularly in highly heterogeneous environments. Second, the framework could be extended to more complex data distributions, such as multimodal data or heterogeneous label spaces, to improve the generalization in multi-task federated learning scenarios. Additionally, applying this framework to large-scale federated learning systems with real-world constraints, such as a limited communication bandwidth and heterogeneous device capabilities, presents an exciting direction. Exploring techniques such as decentralized learning, federated learning with differential privacy, or federated transfer learning could also provide valuable insights for improving both the security and performance of federated systems.  
The authors confirm their contributions to this paper as follows: study conception and design: [PERSON] and Z.Z.; data collection: J.S.; analysis and interpretation of results: A.L. and Z.X.; draft manuscript preparation: [PERSON] and Z.Z.; funding acquisition: Y.L.; manuscript review: Y.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China, grant number 61972334.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.