## Line Graph: Accuracy (%) vs Attack ratio (%) ### Overview The graph compares the accuracy of seven different methods (FedAvg, ShieldFL, PBFL, Median, Biscotti, FoolsGold, Ours) across varying attack ratios (0% to 50%). Accuracy is measured on the y-axis (57.5%–75%), while the x-axis represents the percentage of malicious participants in the system. Each method is represented by a distinct colored line with unique markers. ### Components/Axes - **X-axis (Attack ratio (%))**: Ranges from 0% to 50% in 10% increments. - **Y-axis (Accuracy (%))**: Ranges from 57.5% to 75% in 2.5% increments. - **Legend**: Located at the bottom-left corner, mapping colors/markers to methods: - **FedAvg**: Blue square - **ShieldFL**: Orange diamond - **PBFL**: Green triangle - **Median**: Purple pentagon - **Biscotti**: Gray star - **FoolsGold**: Brown triangle - **Ours**: Red circle ### Detailed Analysis 1. **FedAvg (Blue Square)**: - Starts at ~75% accuracy at 0% attack ratio. - Declines gradually to ~70% at 50% attack ratio. - Maintains relatively stable performance compared to others. 2. **ShieldFL (Orange Diamond)**: - Begins at ~75% accuracy at 0%. - Sharp decline to ~70% by 10% attack ratio. - Stabilizes slightly above 70% for higher attack ratios. 3. **PBFL (Green Triangle)**: - Starts at ~74% accuracy at 0%. - Gradual decline to ~69% at 50%. - Shows moderate resilience to attacks. 4. **Median (Purple Pentagon)**: - Begins at ~68% accuracy at 0%. - Slightly increases to ~69% at 10%, then declines steadily. - Ends at ~63% at 50%, the lowest among all methods. 5. **Biscotti (Gray Star)**: - Starts at ~73% accuracy at 0%. - Sharp decline to ~62% by 20% attack ratio. - Continues dropping to ~57% at 50%, the steepest slope. 6. **FoolsGold (Brown Triangle)**: - Begins at ~73% accuracy at 0%. - Gradual decline to ~68% at 30%. - Accelerates downward to ~62% at 50%. 7. **Ours (Red Circle)**: - Starts at ~75% accuracy at 0%. - Moderate decline to ~70% at 30%. - Stabilizes near 70% for higher attack ratios, outperforming most methods. ### Key Observations - **Best Performance at 0% Attack**: FedAvg, ShieldFL, and Ours all start near 75% accuracy. - **Worst Performance at 50% Attack**: Biscotti drops to ~57%, while Median hits ~63%. - **Resilience**: Ours and FedAvg maintain the highest accuracy across all attack ratios. - **Volatility**: ShieldFL and Biscotti show the most dramatic declines, especially after 10% attack ratio. ### Interpretation The data suggests that **FedAvg** and **Ours** are the most robust methods, maintaining accuracy above 70% even at 50% attack ratios. **ShieldFL** and **PBFL** offer moderate resilience but degrade significantly under high attack conditions. **Biscotti** and **FoolsGold** are highly vulnerable, with steep accuracy drops. The **Median** line indicates that average client performance is poor under attack, highlighting systemic weaknesses. These trends emphasize the importance of attack-aware design in federated learning systems, with **Ours** and **FedAvg** emerging as the most reliable choices for adversarial environments.