## Line Chart: Accuracy vs. Attack Ratio for Different Federated Learning Methods ### Overview The image is a line chart comparing the accuracy of different federated learning methods as the attack ratio increases. The x-axis represents the attack ratio (percentage), and the y-axis represents the accuracy (percentage). Several federated learning methods are compared, including 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 65, 70, 75, 80, 85, 90, and 95. * **Legend:** Located on the left side of the chart, listing the federated learning methods and their corresponding colors/markers: * FedAvg (blue, square marker) * ShieldFL (orange, diamond marker) * PBFL (green, triangle marker) * Median (purple, pentagon marker) * Biscotti (gray, star marker) * FoolsGold (brown, inverted triangle marker) * Ours (red, circle marker) ### Detailed Analysis * **FedAvg (blue, square marker):** Starts at approximately 94% accuracy at 0% attack ratio. The accuracy decreases gradually as the attack ratio increases, reaching approximately 82% at 50% attack ratio. * **ShieldFL (orange, diamond marker):** Starts at approximately 94% accuracy at 0% attack ratio. The accuracy decreases gradually as the attack ratio increases, reaching approximately 82% at 50% attack ratio. * **PBFL (green, triangle marker):** Starts at approximately 94% accuracy at 0% attack ratio. The accuracy decreases gradually as the attack ratio increases, reaching approximately 79% at 50% attack ratio. * **Median (purple, pentagon marker):** Starts at approximately 94% accuracy at 0% attack ratio. The accuracy decreases gradually as the attack ratio increases, reaching approximately 84% at 50% attack ratio. * **Biscotti (gray, star marker):** Starts at approximately 94% accuracy at 0% attack ratio. The accuracy decreases sharply as the attack ratio increases, reaching approximately 65% at 50% attack ratio. * **FoolsGold (brown, inverted triangle marker):** Starts at approximately 94% accuracy at 0% attack ratio. The accuracy decreases sharply as the attack ratio increases, reaching approximately 74% at 50% attack ratio. * **Ours (red, circle marker):** Starts at approximately 95% accuracy at 0% attack ratio. The accuracy decreases slightly as the attack ratio increases, reaching approximately 91% at 50% attack ratio. ### Key Observations * The "Ours" method consistently maintains the highest accuracy across all attack ratios. * The Biscotti method experiences the most significant drop in accuracy as the attack ratio increases. * FedAvg, ShieldFL, PBFL, and Median methods show a moderate decrease in accuracy as the attack ratio increases. * FoolsGold also shows a significant drop in accuracy, though not as severe as Biscotti. ### Interpretation The chart demonstrates the impact of increasing attack ratios on the accuracy of various federated learning methods. The "Ours" method appears to be the most robust against attacks, maintaining a high level of accuracy even at a 50% attack ratio. Biscotti is the most vulnerable, with its accuracy plummeting as the attack ratio increases. The other methods (FedAvg, ShieldFL, PBFL, and Median) exhibit intermediate levels of resilience. This suggests that the "Ours" method incorporates mechanisms that effectively mitigate the effects of adversarial attacks, while Biscotti lacks such defenses. The performance differences highlight the importance of designing federated learning algorithms that are robust to malicious participants or data manipulation.

\n ## Line Chart: Accuracy vs. Attack Ratio for Federated Learning Defenses ### Overview This image presents a line chart comparing the accuracy of several federated learning defense mechanisms under varying attack ratios. The x-axis represents the attack ratio (as a percentage), and the y-axis represents the accuracy (also as a percentage). Six different defense strategies are plotted, allowing for a direct comparison of their resilience to attacks. ### Components/Axes * **X-axis:** "Attack ratio (%)" - Scale ranges from 0% to 50%, with markers at 0, 10, 20, 30, 40, and 50. * **Y-axis:** "Accuracy (%)" - Scale ranges from 65% to 95%, with markers at 65, 70, 75, 80, 85, 90, and 95. * **Legend (bottom-left):** Lists the defense mechanisms and their corresponding line colors: * FedAvg (Blue Square) * ShieldFL (Orange Diamond) * PBFL (Green Triangle) * Median (Purple Circle) * Biscotti (Gray Star) * FoolsGold (Brown Inverted Triangle) * Ours (Red Circle) ### Detailed Analysis Here's a breakdown of each line's trend and approximate data points, verified against the legend colors: * **FedAvg (Blue Square):** The line slopes downward, indicating decreasing accuracy with increasing attack ratio. * 0% Attack: ~94% Accuracy * 10% Attack: ~93% Accuracy * 20% Attack: ~91% Accuracy * 30% Attack: ~88% Accuracy * 40% Attack: ~85% Accuracy * 50% Attack: ~82% Accuracy * **ShieldFL (Orange Diamond):** The line shows a more rapid decline in accuracy compared to FedAvg. * 0% Attack: ~94% Accuracy * 10% Attack: ~92% Accuracy * 20% Attack: ~89% Accuracy * 30% Attack: ~84% Accuracy * 40% Attack: ~78% Accuracy * 50% Attack: ~72% Accuracy * **PBFL (Green Triangle):** The line initially remains relatively stable, then declines sharply after 30% attack ratio. * 0% Attack: ~94% Accuracy * 10% Attack: ~93% Accuracy * 20% Attack: ~91% Accuracy * 30% Attack: ~88% Accuracy * 40% Attack: ~80% Accuracy * 50% Attack: ~75% Accuracy * **Median (Purple Circle):** The line exhibits a moderate downward slope, similar to FedAvg but slightly steeper. * 0% Attack: ~94% Accuracy * 10% Attack: ~92% Accuracy * 20% Attack: ~90% Accuracy * 30% Attack: ~86% Accuracy * 40% Attack: ~83% Accuracy * 50% Attack: ~80% Accuracy * **Biscotti (Gray Star):** This line demonstrates a very steep decline in accuracy, becoming significantly lower than other methods at higher attack ratios. * 0% Attack: ~94% Accuracy * 10% Attack: ~92% Accuracy * 20% Attack: ~88% Accuracy * 30% Attack: ~78% Accuracy * 40% Attack: ~68% Accuracy * 50% Attack: ~65% Accuracy * **FoolsGold (Brown Inverted Triangle):** The line shows a moderate decline in accuracy, similar to Median. * 0% Attack: ~94% Accuracy * 10% Attack: ~93% Accuracy * 20% Attack: ~91% Accuracy * 30% Attack: ~88% Accuracy * 40% Attack: ~84% Accuracy * 50% Attack: ~80% Accuracy * **Ours (Red Circle):** This line exhibits the most stable accuracy across all attack ratios, showing the smallest decline. * 0% Attack: ~94% Accuracy * 10% Attack: ~94% Accuracy * 20% Attack: ~93% Accuracy * 30% Attack: ~91% Accuracy * 40% Attack: ~90% Accuracy * 50% Attack: ~89% Accuracy ### Key Observations * The "Ours" defense mechanism consistently outperforms all other methods across all attack ratios, maintaining a high level of accuracy even under significant attack. * Biscotti is the most vulnerable defense, experiencing a dramatic drop in accuracy as the attack ratio increases. * FedAvg, Median, and FoolsGold show similar performance, with moderate declines in accuracy. * ShieldFL and PBFL exhibit intermediate vulnerability, declining more rapidly than FedAvg but less drastically than Biscotti. ### Interpretation The chart demonstrates the effectiveness of the proposed defense mechanism ("Ours") against attacks in a federated learning environment. The consistently high accuracy suggests that this method is robust and can maintain performance even when a significant portion of the participating clients are compromised. The stark contrast between "Ours" and Biscotti highlights the importance of choosing a resilient defense strategy. The data suggests that the proposed defense is superior in mitigating the impact of attacks on model accuracy. The relatively stable performance of "Ours" could be due to a more sophisticated attack mitigation strategy, such as robust aggregation rules or anomaly detection. The rapid decline of Biscotti suggests it may be susceptible to a specific type of attack or lack sufficient defenses against malicious participants. The chart provides valuable insights for practitioners seeking to deploy secure federated learning systems.

## Line Chart: Accuracy vs. Attack Ratio for Federated Learning Methods ### Overview The image is a line chart comparing the performance (accuracy) of seven different federated learning methods or algorithms as the "Attack ratio" increases. The chart demonstrates how each method's accuracy degrades under increasing adversarial conditions. The overall trend for all methods is a decline in accuracy as the attack ratio rises, but the rate of decline varies significantly. ### Components/Axes * **Chart Type:** Multi-line chart with markers. * **X-Axis:** Labeled **"Attack ratio (%)"**. The scale runs from 0 to 50 in increments of 10 (0, 10, 20, 30, 40, 50). * **Y-Axis:** Labeled **"Accuracy (%)"**. The scale runs from 65 to 95 in increments of 5 (65, 70, 75, 80, 85, 90, 95). * **Legend:** Located in the **bottom-left corner** of the chart area. It lists seven data series with corresponding colors and markers: 1. **FedAvg** - Blue line with square markers (□). 2. **ShieldFL** - Orange line with diamond markers (◇). 3. **PBFL** - Green line with upward-pointing triangle markers (△). 4. **Median** - Purple line with circle markers (○). 5. **Biscotti** - Gray line with star/asterisk markers (☆). 6. **FoolsGold** - Brown line with downward-pointing triangle markers (▽). 7. **Ours** - Red line with circle markers (○). * **Grid:** A light gray grid is present in the background. ### Detailed Analysis The following describes the visual trend for each data series and approximates the data points by reading the chart. Values are approximate due to visual estimation. 1. **Ours (Red line, circle markers):** * **Trend:** The most resilient line. It shows a very gradual, almost flat decline until an attack ratio of 30%, after which it slopes gently downward. * **Approximate Data Points:** (0%, ~95%), (10%, ~95%), (20%, ~95%), (30%, ~94%), (40%, ~92%), (50%, ~90%). 2. **Median (Purple line, circle markers):** * **Trend:** A steady, moderate downward slope across the entire range. * **Approximate Data Points:** (0%, ~95%), (10%, ~94%), (20%, ~93%), (30%, ~90%), (40%, ~87%), (50%, ~85%). 3. **FedAvg (Blue line, square markers):** * **Trend:** A consistent downward slope, slightly steeper than Median after 20%. * **Approximate Data Points:** (0%, ~95%), (10%, ~93%), (20%, ~92%), (30%, ~89%), (40%, ~85%), (50%, ~82%). 4. **ShieldFL (Orange line, diamond markers):** * **Trend:** Follows a path very close to FedAvg, with a nearly identical slope, ending slightly lower. * **Approximate Data Points:** (0%, ~95%), (10%, ~93%), (20%, ~91%), (30%, ~88%), (40%, ~84%), (50%, ~81%). 5. **PBFL (Green line, upward triangle markers):** * **Trend:** A steady decline that accelerates slightly after 30%. * **Approximate Data Points:** (0%, ~95%), (10%, ~93%), (20%, ~91%), (30%, ~87%), (40%, ~83%), (50%, ~79%). 6. **FoolsGold (Brown line, downward triangle markers):** * **Trend:** A moderate decline until 30%, followed by a steeper drop. * **Approximate Data Points:** (0%, ~95%), (10%, ~93%), (20%, ~90%), (30%, ~86%), (40%, ~80%), (50%, ~75%). 7. **Biscotti (Gray line, star markers):** * **Trend:** The steepest and most severe decline of all methods. It begins to drop sharply after 20%. * **Approximate Data Points:** (0%, ~95%), (10%, ~93%), (20%, ~90%), (30%, ~86%), (40%, ~77%), (50%, ~65%). ### Key Observations * **Performance Hierarchy:** At a 0% attack ratio, all methods start at nearly the same high accuracy (~95%). As the attack ratio increases, a clear performance hierarchy emerges: **Ours > Median > FedAvg ≈ ShieldFL > PBFL > FoolsGold > Biscotti**. * **Divergence Point:** The significant divergence in performance begins around an attack ratio of **20%**. Before this point, the lines are tightly clustered. * **Most Robust:** The method labeled **"Ours"** is the most robust, maintaining accuracy above 90% even at a 50% attack ratio. * **Most Vulnerable:** The **Biscotti** method is the most vulnerable, suffering a catastrophic drop to approximately 65% accuracy at a 50% attack ratio. * **Clustering:** FedAvg and ShieldFL perform very similarly throughout the range. PBFL and FoolsGold also follow similar trajectories until the final data point. ### Interpretation This chart is likely from a research paper proposing a new federated learning method (labeled "Ours") designed to be robust against adversarial attacks or Byzantine faults. The "Attack ratio" represents the proportion of malicious or faulty clients in the federated learning network. The data demonstrates that the proposed method ("Ours") significantly outperforms six established baseline methods (FedAvg, ShieldFL, PBFL, Median, Biscotti, FoolsGold) as the environment becomes more hostile. The key finding is that while all methods degrade, "Ours" degrades at a much slower rate, suggesting it incorporates a more effective aggregation rule or defense mechanism. The poor performance of Biscotti at high attack ratios indicates its specific vulnerability to the type of attack simulated here. The relative ordering of the other methods provides a benchmark for comparing their inherent resilience. The chart's primary purpose is to visually and quantitatively argue for the superiority of the authors' proposed solution in maintaining model accuracy under adversarial conditions.

## Line Chart: Accuracy vs. Attack Ratio ### Overview The image is a line chart comparing the accuracy of eight different methods (FedAvg, ShieldFL, PBFL, Median, Biscotti, FoolsGold, Ours) across varying attack ratios (0% to 50%). Accuracy (%) is plotted on the y-axis (65–95%), and attack ratio (%) is on the x-axis (0–50%). Each method is represented by a distinct colored line with a unique marker. ### Components/Axes - **Y-axis**: Accuracy (%) (65–95%), labeled "Accuracy (%)". - **X-axis**: Attack ratio (%) (0–50%), labeled "Attack ratio (%)". - **Legend**: Located in the bottom-left corner, mapping colors/markers to methods: - Blue square: FedAvg - Orange diamond: ShieldFL - Green triangle: PBFL - Purple diamond: Median - Gray star: Biscotti - Brown triangle: FoolsGold - Red circle: Ours ### Detailed Analysis 1. **Ours (Red Circle)**: - Starts at ~95% accuracy at 0% attack ratio. - Declines gradually to ~90% at 50% attack ratio. - Maintains the highest accuracy across all attack ratios. 2. **FedAvg (Blue Square)**: - Begins at ~94% accuracy at 0% attack ratio. - Drops to ~82% at 50% attack ratio. - Shows a steady decline. 3. **ShieldFL (Orange Diamond)**: - Starts at ~94% accuracy at 0% attack ratio. - Declines to ~81% at 50% attack ratio. - Slightly steeper decline than FedAvg. 4. **PBFL (Green Triangle)**: - Begins at ~94% accuracy at 0% attack ratio. - Drops to ~79% at 50% attack ratio. - Steady decline with moderate slope. 5. **Median (Purple Diamond)**: - Starts at ~94% accuracy at 0% attack ratio. - Declines to ~85% at 50% attack ratio. - Gradual slope, similar to FedAvg. 6. **Biscotti (Gray Star)**: - Begins at ~94% accuracy at 0% attack ratio. - Plummets to ~65% at 50% attack ratio. - Sharpest decline among all methods. 7. **FoolsGold (Brown Triangle)**: - Starts at ~94% accuracy at 0% attack ratio. - Drops to ~75% at 50% attack ratio. - Moderate decline, steeper than FedAvg/ShieldFL. ### Key Observations - **Ours** consistently outperforms all other methods, maintaining the highest accuracy even at 50% attack ratio. - **Biscotti** exhibits the most severe degradation, losing ~30% accuracy at maximum attack ratio. - Methods like FedAvg, ShieldFL, and PBFL show similar initial performance but diverge significantly under higher attack ratios. - The red line ("Ours") remains the flattest, indicating robustness against attacks. ### Interpretation The data demonstrates that the method labeled "Ours" is the most resilient to adversarial attacks, retaining ~90% accuracy even at 50% attack ratio. In contrast, Biscotti’s performance collapses under pressure, suggesting it is highly vulnerable. The gradual declines in other methods highlight trade-offs between accuracy and attack resistance. This chart underscores the importance of robust defense mechanisms in federated learning systems, with "Ours" setting a benchmark for performance under adversarial conditions.