## Line Chart: CIFAR-10 Test Accuracy vs. α Parameter ### Overview The chart compares the test accuracy of two machine learning models (FedProto and FedMRL) across varying values of the hyperparameter α (alpha) on the CIFAR-10 dataset. Test accuracy is measured on the y-axis (40-60%), while α ranges from 0.1 to 0.5 on the x-axis. ### Components/Axes - **X-axis (α)**: Discrete values at 0.1, 0.2, 0.3, 0.4, 0.5 - **Y-axis (Test Accuracy)**: Percentage scale from 40% to 60% - **Legend**: Located in the top-right corner, with: - **FedProto**: Dashed teal line with circle markers - **FedMRL**: Solid purple line with star markers ### Detailed Analysis 1. **FedProto (Teal Line)**: - Starts at ~45% accuracy at α=0.1 - Gradually declines to ~40% by α=0.5 - Slight plateau between α=0.3 and α=0.5 (~40-41%) - Trend: Consistent downward slope with minimal fluctuation 2. **FedMRL (Purple Line)**: - Begins at ~65% accuracy at α=0.1 - Drops sharply to ~63% at α=0.3 - Stabilizes between α=0.3 and α=0.5 (~63-64%) - Trend: Initial decline followed by plateau ### Key Observations - FedMRL consistently outperforms FedProto across all α values (20-30% higher accuracy) - FedProto shows a steady degradation as α increases - FedMRL demonstrates robustness to α changes after initial drop - No overlapping confidence intervals between the two models ### Interpretation The data suggests FedMRL is significantly more robust to hyperparameter α variations than FedProto for CIFAR-10 classification. The stability of FedMRL's accuracy after α=0.3 indicates potential insensitivity to this parameter in later training stages. In contrast, FedProto's continuous decline implies α plays a critical role in its performance. These results could guide hyperparameter tuning strategies, with FedMRL being preferable for scenarios requiring α flexibility. The stark performance gap raises questions about FedProto's architectural limitations or training dynamics under varying α conditions.