## Chart: Accuracy vs. Attack Ratio for Different Federated Learning Algorithms
### Overview
The image is a line chart comparing the accuracy of different federated learning algorithms as the attack ratio increases. The x-axis represents the attack ratio (percentage), and the y-axis represents the accuracy (percentage). Several algorithms are compared: FedAvg, ShieldFL, PBFL, Median, Biscotti, FoolsGold, and Ours.
### Components/Axes
* **X-axis:** Attack ratio (%), with markers at 0, 10, 20, 30, 40, and 50.
* **Y-axis:** Accuracy (%), with markers at 0, 20, 40, 60, 80, and 100.
* **Legend:** Located on the left side of the chart, listing the algorithms and their corresponding line colors and markers:
* FedAvg (blue, square marker)
* ShieldFL (orange, diamond marker)
* PBFL (green, triangle marker)
* Median (purple, pentagon marker)
* Biscotti (gray, star marker)
* FoolsGold (brown/dark red, inverted triangle marker)
* Ours (red, circle marker)
### Detailed Analysis
* **FedAvg (blue, square marker):** The accuracy remains relatively stable around 98% until an attack ratio of 40%, then drops sharply to approximately 2% at 50%.
* (0, 98)
* (10, 98)
* (20, 98)
* (30, 97)
* (40, 84)
* (50, 2)
* **ShieldFL (orange, diamond marker):** The accuracy remains relatively stable around 98% until an attack ratio of 40%, then drops sharply to approximately 3% at 50%.
* (0, 98)
* (10, 97)
* (20, 98)
* (30, 97)
* (40, 84)
* (50, 3)
* **PBFL (green, triangle marker):** The accuracy remains relatively stable around 98% until an attack ratio of 40%, then drops sharply to approximately 3% at 50%.
* (0, 98)
* (10, 97)
* (20, 97)
* (30, 92)
* (40, 83)
* (50, 3)
* **Median (purple, pentagon marker):** The accuracy remains relatively stable around 98% until an attack ratio of 40%, then drops sharply to approximately 2% at 50%.
* (0, 98)
* (10, 97)
* (20, 98)
* (30, 97)
* (40, 84)
* (50, 2)
* **Biscotti (gray, star marker):** The accuracy drops sharply from approximately 98% at 20% attack ratio to 0% at 30% attack ratio, remaining at 0% for higher attack ratios.
* (0, 98)
* (10, 97)
* (20, 98)
* (30, 0)
* (40, 0)
* (50, 0)
* **FoolsGold (brown/dark red, inverted triangle marker):** The accuracy remains relatively stable around 98% until an attack ratio of 40%, then drops sharply to approximately 5% at 50%.
* (0, 98)
* (10, 98)
* (20, 98)
* (30, 98)
* (40, 98)
* (50, 5)
* **Ours (red, circle marker):** The accuracy remains relatively stable around 98% until an attack ratio of 40%, then drops to approximately 72% at 50%.
* (0, 98)
* (10, 98)
* (20, 98)
* (30, 98)
* (40, 98)
* (50, 72)
### Key Observations
* The Biscotti algorithm is highly vulnerable to attacks, with its accuracy dropping to 0% at a 30% attack ratio.
* The "Ours" algorithm maintains the highest accuracy at a 50% attack ratio compared to the other algorithms.
* FedAvg, ShieldFL, PBFL, and Median algorithms exhibit similar performance, maintaining high accuracy until a 40% attack ratio, after which their accuracy drops sharply.
* FoolsGold maintains high accuracy until a 40% attack ratio, then drops sharply, but not as low as FedAvg, ShieldFL, PBFL, and Median.
### Interpretation
The chart demonstrates the vulnerability of different federated learning algorithms to attacks. The "Ours" algorithm appears to be the most robust against attacks, maintaining a significantly higher accuracy at a 50% attack ratio compared to the other algorithms. Biscotti is the least robust, failing at a lower attack ratio. The other algorithms show similar vulnerabilities, with a sharp decline in accuracy beyond a 40% attack ratio. This suggests that the "Ours" algorithm has a mechanism to mitigate the impact of attacks, making it a potentially more reliable choice in adversarial environments.
