## Line Chart: Accuracy vs. Attack Ratio for Federated Learning Defenses
### Overview
This line chart depicts the accuracy of several Federated Learning (FL) defense mechanisms as a function of the attack ratio. The chart compares the performance of FedAvg, ShieldFL, PBFL, Median, Biscotti, FoolsGold, and a method labeled "Ours". Accuracy is measured in percentage, and the attack ratio is also expressed as a percentage.
### Components/Axes
* **X-axis:** "Attack ratio (%)" - Ranges from 0% to 50%, with markers at 0, 10, 20, 30, 40, and 50.
* **Y-axis:** "Accuracy (%)" - Ranges from 0% to 100%, with markers at 0, 20, 40, 60, 80, and 100.
* **Legend:** Located in the bottom-left corner, listing the following defense mechanisms with corresponding line colors:
* FedAvg (Blue)
* ShieldFL (Orange)
* PBFL (Green)
* Median (Purple)
* Biscotti (Grey)
* FoolsGold (Red)
* Ours (Brown/Maroon)
### Detailed Analysis
Here's a breakdown of each line's trend and approximate data points, verified against the legend colors:
* **FedAvg (Blue):** The line starts at approximately 98% accuracy at 0% attack ratio. It remains relatively stable until approximately 30% attack ratio, where it begins a steep decline, reaching approximately 2% accuracy at 50% attack ratio.
* **ShieldFL (Orange):** Starts at approximately 96% accuracy at 0% attack ratio. It remains stable until approximately 30% attack ratio, then declines to approximately 10% accuracy at 50% attack ratio.
* **PBFL (Green):** Starts at approximately 98% accuracy at 0% attack ratio. It remains stable until approximately 30% attack ratio, then declines to approximately 85% accuracy at 40% attack ratio and approximately 0% accuracy at 50% attack ratio.
* **Median (Purple):** Starts at approximately 98% accuracy at 0% attack ratio. It remains stable until approximately 30% attack ratio, then declines to approximately 80% accuracy at 40% attack ratio and approximately 0% accuracy at 50% attack ratio.
* **Biscotti (Grey):** Starts at approximately 96% accuracy at 0% attack ratio. It remains stable until approximately 30% attack ratio, then declines to approximately 80% accuracy at 40% attack ratio and approximately 0% accuracy at 50% attack ratio.
* **FoolsGold (Red):** Starts at approximately 98% accuracy at 0% attack ratio. It remains stable until approximately 30% attack ratio, then declines to approximately 70% accuracy at 40% attack ratio and approximately 0% accuracy at 50% attack ratio.
* **Ours (Brown/Maroon):** Starts at approximately 99% accuracy at 0% attack ratio. It remains stable until approximately 40% attack ratio, then declines to approximately 70% accuracy at 50% attack ratio.
### Key Observations
* All defense mechanisms exhibit a significant drop in accuracy as the attack ratio increases.
* The "Ours" method maintains higher accuracy than the other methods at higher attack ratios (40-50%).
* The most significant accuracy drops occur between 30% and 50% attack ratio for most methods.
* FedAvg, ShieldFL, PBFL, Median, Biscotti, and FoolsGold all converge to near 0% accuracy at a 50% attack ratio.
### Interpretation
The chart demonstrates the vulnerability of Federated Learning models to attacks, as evidenced by the declining accuracy with increasing attack ratios. The "Ours" method appears to be more robust against attacks, maintaining a higher level of accuracy even at a 50% attack ratio compared to the other defense mechanisms. This suggests that the "Ours" method provides a more effective defense against the type of attacks being simulated. The sharp decline in accuracy for all methods between 30% and 50% attack ratio indicates a critical threshold where the attacks become significantly more effective. The convergence of most methods to near-zero accuracy at 50% suggests a complete compromise of the model's integrity under a high attack load. The data suggests that while all defenses offer some initial protection, they are ultimately susceptible to attacks, and further research is needed to develop more resilient FL defense strategies.
