## 4 Experimental Evaluation
### Experimental Results and Analysis
#### 4.2.1 Performance Metric Evaluation Based on Four Datasets  
To evaluate the performance of the proposed Privacy-Preserving Federated Learning method (PPFL-SAGIN) in different scenarios, we validated it through image recognition tasks using the MNIST dataset, the EuroSAT dataset, and the UC Merced Land Use dataset. Figure 4 shows the performance of the PPFL-SAGIN, FedAvg, FedAsync, and FedAsyncISL based on the MNIST dataset. Figures 3(a), 4(a), 4(a) and 4(a) illustrate the accuracy performance of the PPFL-SAGIN, FedAvg, FedAsync, and FedAsyncISL on the MNIST, EuroSAT, UC Merced Land Use, and RSI-CB datasets. It is evident that the PPFL-SAGIN achieved the highest accuracy across all rounds and datasets, with FedAsyncISL consistently ranking second, its accuracy values lying between those of the PPFL-SAGIN and the standard FedAsync method. FedAsync outperformed FedAvg but could not reach the level of the PPFL-SAGIN or FedAsyncISL. In terms of convergence, the PPFL-SAGIN not only achieved higher accuracy but also converged faster, especially in the early rounds, where its learning curve was steeper. All methods exhibited diminishing marginal returns after approximately 6-7 rounds, with the accuracy curves flattening, though the PPFL-SAGIN maintained its advantage. The consistent superiority of the PPFL-SAGIN across multiple datasets indicates that its approach to handling asynchronous updates and client selection provides significant advantages in federated learning scenarios. Figures 3(b), 4(b), 4(b) and 4(b) illustrate the precision performance of the PPFL-SAGIN, FedAvg, FedAsync, and FedAsyncISL on the MNIST, EuroSAT, UC Merced Land Use and RSI-CB datasets. The PPFL-SAGIN achieved the highest precision across all rounds and datasets, followed by FedAsyncISL, which significantly outperformed the basic asynchronous method. FedAsync performed better than the traditional federated averaging method, while FedAvg exhibited the lowest performance in all scenarios. In the early training stages (rounds 1-3), the PPFL-SAGIN demonstrated significantly higher precision compared to other methods, indicating that its adaptive knowledge sharing mechanism can quickly adapt to data distribution differences among different devices, accelerating model convergence. Maintaining a stable lead across four datasets of varying complexity demonstrates that its dynamic weighting factors effectively balance device heterogeneity and data distribution differences, enabling the model to perform excellently in different application scenarios. In later rounds (6-10), the PPFL-SAGIN continued to show an upward trend while the other methods plateaued, suggesting that its knowledge sharing mechanism can continuously discover and integrate valuable features, avoiding premature convergence to local optima. Figure 5 shows the performance of PPFL-SAGIN, FedAvg, FedAsync, and FedAsyncISL based on the EuroSAT dataset. Figure 6 shows the performance of PPFL-SAGIN, FedAvg, FedAsync, and FedAsyncISL based on the UC Merced Land Use dataset. Figure 4: Performance of PPFL-SAGIN, FedAvg, FedAsync, and FedAsyncISL based on the MNIST dataset. Figure 5: Performance of PPFL-SAGIN, FedAvg, FedAsync, and FedAsyncISL based on the EuroSAT dataset. Figure 7 shows the performance of PPFL-SAGIN, FedAvg, FedAsync, and FedAsyncLSL based on the RSI-CB dataset. Figures 4c, 5c, 6c and 7c illustrate the recall curves for the PPFL-SAGIN, FedAvg, FedAsync, and FedAsyncLSL on the MNIST, EuroSAT, UC Merced Land Use, and RSI-CB datasets. The PPFL-SAGIN performed best across all datasets and training rounds, with FedAsyncLSL ranking second but showing a significant gap compared to the PPFL-SAGIN. Figure 6: Performance of PPFL-SAGIN, FedAvg, FedAsync, and FedAsyncLSL based on the UC Merced Land Use dataset. Figure 7: Performance of PPFL-SAGIN, FedAvg, FedAsync, and FedAsyncLSL based on the RSI-CB dataset. FedAsync outperformed the traditional FedAvg method, while FedAvg showed the weakest performance, particularly on complex datasets.