\n ## Line Chart: Test Accuracy vs. Communication Round ### Overview This line chart depicts the test accuracy of two federated learning methods, FedProto and FedMRL, over a series of communication rounds. The chart is titled "N=10, CIFAR-100", indicating the number of clients (N=10) and the dataset used (CIFAR-100). ### Components/Axes * **X-axis:** Communication Round, ranging from 0 to 500, with tick marks at 0, 200, and 400. * **Y-axis:** Test Accuracy, ranging from 0 to 80, with tick marks at 20, 40, 60, and 80. * **Legend:** Located in the bottom-center of the chart. * FedProto (represented by a teal/cyan dashed line with circle markers) * FedMRL (represented by a purple dashed line with star markers) * **Title:** "N=10, CIFAR-100" positioned at the top-center of the chart. * **Gridlines:** Vertical dashed gridlines are present to aid in reading values. ### Detailed Analysis * **FedProto (Teal/Cyan Line):** The line starts at approximately 20% test accuracy at Communication Round 0. It exhibits a steep upward slope initially, reaching approximately 65% accuracy around Communication Round 100. The slope then gradually decreases, leveling off around 75% accuracy between Communication Rounds 300 and 500. * Round 0: ~20% * Round 100: ~65% * Round 200: ~71% * Round 300: ~74% * Round 400: ~75% * Round 500: ~75% * **FedMRL (Purple Line):** The line starts at approximately 55% test accuracy at Communication Round 0. It shows a rapid increase initially, reaching approximately 78% accuracy around Communication Round 50. The slope then decreases significantly, leveling off around 78-79% accuracy between Communication Rounds 200 and 500. * Round 0: ~55% * Round 50: ~78% * Round 100: ~79% * Round 200: ~79% * Round 300: ~79% * Round 400: ~79% * Round 500: ~79% ### Key Observations * FedMRL consistently outperforms FedProto across all communication rounds. * Both methods exhibit diminishing returns in terms of accuracy improvement as the number of communication rounds increases. * FedMRL reaches a higher plateau in accuracy compared to FedProto. * FedProto shows a more gradual increase in accuracy over time. ### Interpretation The chart demonstrates the performance of two federated learning algorithms, FedProto and FedMRL, on the CIFAR-100 dataset with 10 clients. FedMRL achieves higher test accuracy and converges faster than FedProto. The leveling off of both curves suggests that further communication rounds yield minimal improvements in accuracy, indicating a point of diminishing returns. The initial rapid increase in accuracy for both methods likely represents the initial learning phase where the models quickly adapt to the data. The difference in performance between the two methods suggests that FedMRL's approach to federated learning is more effective for this specific dataset and client configuration. The fact that FedMRL starts at a higher accuracy suggests it may be less sensitive to initial model conditions or have a more efficient learning process. The chart provides evidence supporting the claim that FedMRL is a superior method for federated learning in this scenario.