The growing usage of federated learning in healthcare opens new prospects for privacy- preserving analytics over heterogeneous data. This work introduces a new Federated Autoencoder-boosted Fuzzy C-Means (FCM) clustering architecture for health risk stratification for non-independent and identically distributed (non-IID) client settings. A synthetic 500-sample dataset generated in line with the UCI Obesity dataset was split evenly across five federated clients based on BMI quantiles to simulate true- world heterogeneity. The autoencoder was used to compress lifestyle and anthropometric high-dimensional features into a two-dimensional latent space, in which FCM was used to get soft, interpretable risk clusters. The proposed method consistently achieves higher clustering scores than the baseline methods across repeated runs. That is, it had the best Silhouette score of 0.600 compared to 0.327 with PCA + KMeans, having more compact and clearly separated clusters. Likewise, it achieved the minimum Davies–Bouldin Index of 0.549, indicating better compactness and minimum overlap compared to PCA + KMeans (0.909) and PCA + FCM (0.954). The Calinski–Harabasz Index also established strength with a value of 1182.0, significantly greater than PCA + KMeans (323.3) and using raw features as the basis for clustering (31.8). Client-wise analysis revealed uniform clustering quality, with Silhouette values between 0.573 and 0.659 across all five clients, demonstrating stable clustering performance under the simulated heterogeneous client distributions considered in this study. These results affirm that the combination of nonlinear latent feature extraction and fuzzy clustering results in high-quality, interpretable risk stratification while preserving data locality within the federated learning framework.
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