Fig. 2: Comparative analysis of federated learning model performance. | npj Digital Medicine

Fig. 2: Comparative analysis of federated learning model performance.

From: Personalized federated learning for predicting disability progression in multiple sclerosis using real-world routine clinical data

Fig. 2

Evaluating ROC–AUC and AUC–PR Metrics Across Different Strategies. (Left) Receiver Operating Characteristic: The centralized model achieves the highest performance with an ROC–AUC = 0.8092 ± 0.0012, demonstrating the advantage of having a pooled dataset. Among FL strategies, FedAdam and FedYogi perform best, with ROC–AUC values of 0.7920 ± 0.0031 and 0.7910 ± 0.0028, respectively. The other FL methods, including FedAvg and FedProx, show slightly lower performance, underscoring the challenges of a global federated model in heterogeneous data settings. (Right) Precision-Recall Curve: Again, the centralized model outperforms with an AUC–PR = 0.4605 ± 0.0043. Among FL methods, FedAdam achieves the highest AUC–PR of 0.4488 ± 0.0061, while FedYogi and FedProx follow closely with values of 0.4420 ± 0.0078 and 0.4081 ± 0.0058, respectively. The drop in performance compared to the centralized approach reflects the difficulty of capturing minority class predictions in federated settings. These results emphasize the performance gap between centralized and federated learning strategies, particularly in heterogeneous and imbalanced data scenarios.

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