\n
## Line Chart: Test Accuracy vs. Communication Round
### Overview
This chart displays the test accuracy of two methods, "Standalone" and "FedMRL", over a series of communication rounds. The chart is titled "N=50, CIFAR-100", indicating the parameters used in the experiment. The x-axis represents the communication round, and the y-axis represents the test accuracy.
### Components/Axes
* **Title:** N=50, CIFAR-100 (Top-center)
* **X-axis:** Communication Round (Bottom-center). Scale ranges from 0 to 500, with markers at 0, 100, 200, 300, 400, and 500.
* **Y-axis:** Test Accuracy (Left-center). Scale ranges from 0 to 70, with markers at 0, 20, 40, and 60.
* **Legend:** Located in the bottom-right corner.
* Standalone (Orange, square marker)
* FedMRL (Purple, star marker)
* **Data Series 1:** Standalone (Orange line with square markers)
* **Data Series 2:** FedMRL (Purple line with star markers)
### Detailed Analysis
**Standalone (Orange):**
The orange line representing "Standalone" starts at approximately 45 at Communication Round 0. It increases rapidly to approximately 55 at Communication Round 100. After round 100, the line plateaus, fluctuating around 55-60.
* Round 0: ~45
* Round 100: ~55
* Round 200: ~57
* Round 300: ~58
* Round 400: ~59
* Round 500: ~60
**FedMRL (Purple):**
The purple line representing "FedMRL" starts at approximately 15 at Communication Round 0. It increases rapidly to approximately 55 at Communication Round 100, mirroring the Standalone method's initial increase. After round 100, the line also plateaus, fluctuating around 60-65.
* Round 0: ~15
* Round 100: ~55
* Round 200: ~60
* Round 300: ~62
* Round 400: ~63
* Round 500: ~64
### Key Observations
* Both methods show a significant increase in test accuracy during the first 100 communication rounds.
* After 100 rounds, the accuracy of both methods plateaus, with only minor fluctuations.
* FedMRL consistently achieves a higher test accuracy than Standalone after the initial 100 rounds.
* The initial accuracy of FedMRL is significantly lower than Standalone.
### Interpretation
The data suggests that both the "Standalone" and "FedMRL" methods are effective in improving test accuracy, but "FedMRL" ultimately outperforms "Standalone" after an initial learning phase. The initial lower accuracy of "FedMRL" could be due to the overhead of the federated learning process, requiring initial communication rounds to synchronize and stabilize. The plateauing of both curves after 100 rounds indicates that the models are converging and further communication rounds yield diminishing returns. The parameters "N=50, CIFAR-100" suggest that the experiment was conducted with 50 clients and the CIFAR-100 dataset, a common benchmark for image classification. The difference in performance between the two methods could be attributed to the benefits of federated learning, such as increased data diversity and privacy preservation. The chart demonstrates the convergence of both models, and the superior performance of FedMRL.
