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ResearchFormerly WizWand

Federated Learning on CIFAR-10 (train)

9.82Time per Round (s)FedAvg
9.656810.758411.8612.9616Sep 27, 2024
Updated 8mo ago

Evaluation results

MethodLinks
FedAvgBackbone=LeNet, Iterations per round=100, Hardware=NVIDIA GeForce RTX 2080 Ti, Platform=Pytorch 2.0.1, CUDA version=11.72024.099.821
SCAFFOLDBackbone=LeNet, Iterations per round=100, Hardware=NVIDIA GeForce RTX 2080 Ti, Platform=Pytorch 2.0.1, CUDA version=11.72024.0911.421.16
A-FedPDBackbone=LeNet, Iterations per round=100, Hardware=NVIDIA GeForce RTX 2080 Ti, Platform=Pytorch 2.0.1, CUDA version=11.72024.0911.711.19
FedDynBackbone=LeNet, Iterations per round=100, Hardware=NVIDIA GeForce RTX 2080 Ti, Platform=Pytorch 2.0.1, CUDA version=11.72024.0911.761.19
FedSAMBackbone=LeNet, Iterations per round=100, Hardware=NVIDIA GeForce RTX 2080 Ti, Platform=Pytorch 2.0.1, CUDA version=11.72024.0912.531.27
FedSpeedBackbone=LeNet, Iterations per round=100, Hardware=NVIDIA GeForce RTX 2080 Ti, Platform=Pytorch 2.0.1, CUDA version=11.72024.0913.611.38
A-FedPDSAMBackbone=LeNet, Iterations per round=100, Hardware=NVIDIA GeForce RTX 2080 Ti, Platform=Pytorch 2.0.1, CUDA version=11.72024.0913.91.41