Medical Image Classification on BreaKHis downsampling half (x2↓)
89.63AccuracyMH-pFLGB
Evaluation Results
| Method | Links | ||
|---|---|---|---|
| MH-pFLGBModel Architecture=Heterogeneous (ResNet17, 11, 8, 5)2024.06 | 89.63 | 87.45 | |
| KT-pFLModel Architecture=Heterogeneous (ResNet17, 11, 8, 5)2024.06 | 84.41 | 80.11 | |
| pFedDFModel Architecture=Heterogeneous (ResNet17, 11, 8, 5)2024.06 | 83.69 | 79.65 | |
| DS-PFLModel Architecture=Heterogeneous (ResNet17, 11, 8, 5)2024.06 | 83.34 | 79.67 | |
| FedMDModel Architecture=Heterogeneous (ResNet17, 11, 8, 5)2024.06 | 83.21 | 78.29 | |
| SCAFFOLD+FTModel Architecture=Unified (ResNet17), Fine-tuned=true2024.06 | 82.37 | 77.09 | |
| FedRepModel Architecture=Unified (ResNet17)2024.06 | 82.29 | 76.97 | |
| FedDFModel Architecture=Heterogeneous (ResNet17, 11, 8, 5)2024.06 | 81.32 | 76.29 | |
| FedAvg+FTModel Architecture=Unified (ResNet17), Fine-tuned=true2024.06 | 81.24 | 75.11 | |
| SCAFFOLDModel Architecture=Unified (ResNet17)2024.06 | 80.97 | 75.33 | |
| FedProx+FTModel Architecture=Unified (ResNet17), Fine-tuned=true2024.06 | 80.55 | 75.49 | |
| Only Local TrainingModel Architecture=Heterogeneous (ResNet17, 11, 8, 5)2024.06 | 80.27 | 74.61 | |
| APFLModel Architecture=Unified (ResNet17)2024.06 | 79.92 | 73.55 | |
| FedAvgModel Architecture=Unified (ResNet17)2024.06 | 79.08 | 74.05 | |
| FedProxModel Architecture=Unified (ResNet17)2024.06 | 78.73 | 74.21 | |
| DittoModel Architecture=Unified (ResNet17)2024.06 | 76.61 | 64.82 | |
| LG-FedAvgModel Architecture=Unified (ResNet17)2024.06 | 56.55 | 43.97 |