## Line Chart: Test Accuracy vs. Communication Round for FedProto and FedMRL
### Overview
The image is a line chart comparing the test accuracy of two federated learning algorithms, FedProto and FedMRL, over a number of communication rounds. The chart displays the performance of these algorithms on the CIFAR-100 dataset with N=10.
### Components/Axes
* **Title:** N=10, CIFAR-100
* **X-axis:** Communication Round, with ticks at 0, 200, and 400.
* **Y-axis:** Test Accuracy, ranging from 20 to 60, with ticks at 20, 40, and 60.
* **Legend:** Located on the right side of the chart.
* FedProto: Represented by a dashed teal line with circle markers.
* FedMRL: Represented by a solid purple line with star markers.
### Detailed Analysis
* **FedProto (Dashed Teal Line with Circle Markers):**
* The line starts at approximately 17% accuracy at communication round 0.
* The accuracy increases rapidly until around communication round 200, reaching approximately 65%.
* After round 200, the accuracy continues to increase, but at a slower rate, reaching approximately 70% at round 500.
* Data points: (0, ~17), (100, ~52), (200, ~65), (300, ~68), (400, ~69), (500, ~70)
* **FedMRL (Solid Purple Line with Star Markers):**
* The line starts at approximately 50% accuracy at communication round 0.
* The accuracy increases rapidly and plateaus around 75% before communication round 100.
* The accuracy remains relatively stable around 75% for the rest of the communication rounds.
* Data points: (0, ~50), (50, ~73), (100, ~75), (200, ~75), (300, ~75), (400, ~75), (500, ~75)
### Key Observations
* FedMRL starts with a significantly higher initial accuracy than FedProto.
* FedMRL converges much faster than FedProto, reaching a stable accuracy level early on.
* FedMRL achieves a higher final accuracy compared to FedProto.
### Interpretation
The chart demonstrates that FedMRL outperforms FedProto in terms of both initial accuracy and convergence speed on the CIFAR-100 dataset with N=10. FedMRL's rapid convergence suggests it may be more efficient in scenarios where communication rounds are limited. The higher final accuracy of FedMRL indicates a better overall performance in this specific setting. The data suggests that FedMRL is a more effective federated learning algorithm for this particular task and dataset configuration.
