Image Classification on CIFAR-10 (test) (Efficiency Metrics)
68.99AccuracyCo-Boosting
Evaluation results
| Method | Links | ||
|---|---|---|---|
| Co-BoostingBackbone=ResNet-18, alpha=0.52024.12 | 68.99 | — | |
| DENSEBackbone=ResNet-18, alpha=0.52024.12 | 66.05 | — | |
| FedSD2CBackbone=ResNet-18, ipc=500, alpha=0.52024.12 | 65.06 | — | |
| FedSD2CBackbone=ResNet-18, ipc=500, alpha=0.32024.12 | 62.37 | — | |
| Co-BoostingBackbone=ResNet-18, alpha=0.32024.12 | 61.75 | — | |
| FedSD2CBackbone=ResNet-18, ipc=500, alpha=0.12024.12 | 56.95 | — | |
| DENSEBackbone=ResNet-18, alpha=0.32024.12 | 54.53 | — | |
| Co-BoostingBackbone=ResNet-18, alpha=0.12024.12 | 53.33 | — | |
| FedSD2CBackbone=ResNet-18, ipc=50, alpha=0.52024.12 | 51.14 | — | |
| FedSD2CBackbone=ResNet-18, ipc=50, alpha=0.32024.12 | 48.23 | — | |
| DENSEBackbone=ResNet-18, alpha=0.12024.12 | 47.75 | — | |
| FedSD2CBackbone=ResNet-18, ipc=50, alpha=0.12024.12 | 44.72 | — |