Federated Learning Image Classification on CIFAR-10 IID
99.42AccuracyFedACT
Evaluation results
| Method | Links | ||
|---|---|---|---|
| FedACTModel=AlexNet, Target Accuracy=0.99332026.03 | 99.42 | 13.58 | |
| MJ-FLModel=AlexNet, Target Accuracy=0.99332026.03 | 99.39 | 21.9 | |
| GeneticModel=AlexNet, Target Accuracy=0.99332026.03 | 99.38 | 20.28 | |
| RandomModel=AlexNet, Target Accuracy=0.99332026.03 | 99.37 | 37.02 | |
| GreedyModel=AlexNet, Target Accuracy=0.99332026.03 | 98.34 | — | |
| FedACTModel=CNN, Target Accuracy=0.8672026.03 | 87.1 | 21.08 | |
| RandomModel=CNN, Target Accuracy=0.8672026.03 | 86.8 | 123.1 | |
| GreedyModel=CNN, Target Accuracy=0.8672026.03 | 86.8 | 36.88 | |
| MJ-FLModel=CNN, Target Accuracy=0.8672026.03 | 86.8 | 31.35 | |
| GeneticModel=CNN, Target Accuracy=0.8672026.03 | 86.7 | 40.08 | |
| FedACTModel=ResNet, Target Accuracy=0.742026.03 | 79.6 | 11.42 | |
| RandomModel=ResNet, Target Accuracy=0.742026.03 | 78.6 | 64.66 | |
| MJ-FLModel=ResNet, Target Accuracy=0.742026.03 | 78.4 | 16.65 | |
| GeneticModel=ResNet, Target Accuracy=0.742026.03 | 75.3 | 31.26 | |
| GreedyModel=ResNet, Target Accuracy=0.742026.03 | 74.2 | 52.68 |