# Research into Robust Federated Learning Methods Driven by Heterogeneity Awareness
## 4 Experiment
### Practical Application Expansion and Critical Analysis  
#### 4.5.1 Practical Application Scenarios  
In summary, extensive experiments conducted on several benchmark datasets--CIFAR-10, SVHN, MNIST, and NotMNIST--demonstrate the effectiveness and robustness of the proposed heterogeneity-aware federated learning framework. In comparative experiments, the proposed method consistently outperforms mainstream federated learning approaches such as FedAvg, FedProx, FedSAM, and FedMOON under varying degrees of data heterogeneity, achieving a higher classification accuracy and lower training loss across multiple datasets. These results highlight its superior generalization capability and convergence efficiency.  
Medical imaging combined diagnosis: In a cross-hospital brain MRI collaboration scenario, the heterogeneity perception mechanism of HAD can coordinate the distribution differences between institutions through feature statistics and manifold analysis and effectively overcome the model bias problem caused by FedAvg's ignorance of data heterogeneity. Compared with FedProx, which relies on a single regularization  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Description**} & **Heterogeneity** & **Center** & **Weighted** & \multirow{2}{*}{**ACC (\%)**} & \multirow{2}{*}{**Loss**} \\  & & **Loss** & **Constraint** & **Aggregation** & & \\ \hline Method1 & Without heterogeneity loss & ✗ & ✗ & ✓ & 47.63 & 1.6466 \\ \hline Method2 & Without feature center constraint & ✓ & ✗ & ✓ & 52.14 & 1.3859 \\ \hline Method3 & Replacing weighted aggregation with simple mean & ✓ & ✗ & ✗ & 51.31 & 1.4412 \\ \hline Method4 & Full method (all modules included) & ✓ & ✓ & ✓ & 53.74 & 1.3683 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Ablation study of key components.  
constraint, the multi-scale modeling of HAD solves the feature space shift more comprehensively. Compared with the contrastive learning mechanism of FedMOON, which requires a frequent exchange of feature vectors, its weighted aggregation significantly reduces the communication burden and provides an efficient solution for distributed medical diagnosis.
* Anomaly detection in the industrial IoT: In the face of differences in operating conditions in multi-factory equipment monitoring, the feature center constraint of HAD maintains cross-domain feature consistency and avoids the amplification effect of FedSAM gradient disturbance on sensor noise. Its heterogeneous weighted aggregation mechanism accurately screens effective client updates, significantly alleviates the convergence delay problem caused by invalid nodes, and provides stable support for industrial equipment condition monitoring in strong distribution difference environments.
* Modeling of financial cross-domain risk control: In inter-bank anti-fraud cooperation, the manifold heterogeneity measure of HAD deeply captures regional nonlinear patterns and breaks through the limitations of FedMOON in complex distribution modeling. By fusing feature center constraint and multi-loss design, the modeling stability of the proposed method is significantly better than that of FedSAM's gradient perturbation strategy, and it realizes the cross-regional collaborative perception of fraud features and provides a robustness of distribution for financial risk control scenarios.