Federated Learning on CIFAR-10 (train)
9.82Time per Round (s)FedAvg
Evaluation Results
| Method | Links | ||
|---|---|---|---|
| FedAvgBackbone=LeNet, Iterations per round=100, Hardware=NVIDIA GeForce RTX 2080 Ti, Platform=Pytorch 2.0.1, CUDA version=11.72024.09 | 9.82 | 1 | |
| SCAFFOLDBackbone=LeNet, Iterations per round=100, Hardware=NVIDIA GeForce RTX 2080 Ti, Platform=Pytorch 2.0.1, CUDA version=11.72024.09 | 11.42 | 1.16 | |
| A-FedPDBackbone=LeNet, Iterations per round=100, Hardware=NVIDIA GeForce RTX 2080 Ti, Platform=Pytorch 2.0.1, CUDA version=11.72024.09 | 11.71 | 1.19 | |
| FedDynBackbone=LeNet, Iterations per round=100, Hardware=NVIDIA GeForce RTX 2080 Ti, Platform=Pytorch 2.0.1, CUDA version=11.72024.09 | 11.76 | 1.19 | |
| FedSAMBackbone=LeNet, Iterations per round=100, Hardware=NVIDIA GeForce RTX 2080 Ti, Platform=Pytorch 2.0.1, CUDA version=11.72024.09 | 12.53 | 1.27 | |
| FedSpeedBackbone=LeNet, Iterations per round=100, Hardware=NVIDIA GeForce RTX 2080 Ti, Platform=Pytorch 2.0.1, CUDA version=11.72024.09 | 13.61 | 1.38 | |
| A-FedPDSAMBackbone=LeNet, Iterations per round=100, Hardware=NVIDIA GeForce RTX 2080 Ti, Platform=Pytorch 2.0.1, CUDA version=11.72024.09 | 13.9 | 1.41 |