Federated Learning Image Classification on CIFAR-10 Non-IID
99Convergence AccuracyMJ-FL
Evaluation results
| Method | Links | ||
|---|---|---|---|
| MJ-FLModel=AlexNet, Target Accuracy=0.9762026.03 | 99 | 59.62 | |
| FedACTModel=AlexNet, Target Accuracy=0.9762026.03 | 99 | 48.37 | |
| RandomModel=AlexNet, Target Accuracy=0.9762026.03 | 98.9 | 141.5 | |
| GeneticModel=AlexNet, Target Accuracy=0.9762026.03 | 98.5 | 60.72 | |
| GreedyModel=AlexNet, Target Accuracy=0.9762026.03 | 87.2 | 179.9 | |
| FedACTModel=CNN, Target Accuracy=0.732026.03 | 84.3 | 12.83 | |
| MJ-FLModel=CNN, Target Accuracy=0.732026.03 | 83 | 16.51 | |
| RandomModel=CNN, Target Accuracy=0.732026.03 | 82.3 | 46.82 | |
| GeneticModel=CNN, Target Accuracy=0.732026.03 | 76.8 | 22.95 | |
| GreedyModel=CNN, Target Accuracy=0.732026.03 | 76.4 | 71.38 | |
| FedACTModel=ResNet, Target Accuracy=0.502026.03 | 58.4 | 166.8 | |
| MJ-FLModel=ResNet, Target Accuracy=0.502026.03 | 56.8 | 218.4 | |
| RandomModel=ResNet, Target Accuracy=0.502026.03 | 54.7 | 851.7 | |
| GeneticModel=ResNet, Target Accuracy=0.502026.03 | 48.7 | 623.5 | |
| GreedyModel=ResNet, Target Accuracy=0.502026.03 | 40.4 | — |