Image Classification on CIFAR100 (Communication Efficiency)
67.9AccuracyFedMoSWA
Evaluation Results
| Method | Links | |||||
|---|---|---|---|---|---|---|
| FedMoSWABackbone=ResNet-18, Data Heterogeneity (Dirichlet coefficient)=0.6, Max Rounds=1000R, Target Accuracy=55%2025.07 | 67.9 | — | — | — | 330 | |
| FedMoSWABackbone=ResNet-18, Data Heterogeneity (Dirichlet coefficient)=0.3, Max Rounds=1000R, Target Accuracy=55%2025.07 | 66.2 | — | — | — | 468 | |
| FedMoSWABackbone=ResNet-18, Data Heterogeneity (Dirichlet coefficient)=0.1, Max Rounds=1000R, Target Accuracy=55%2025.07 | 61.9 | — | — | — | 577 | |
| FedACGBackbone=ResNet-18, Data Heterogeneity (Dirichlet coefficient)=0.6, Max Rounds=1000R, Target Accuracy=55%2025.07 | 61.7 | — | — | — | 518 | |
| MoFedSAMBackbone=ResNet-18, Data Heterogeneity (Dirichlet coefficient)=0.6, Max Rounds=1000R, Target Accuracy=55%2025.07 | 60.1 | — | — | — | 603 | |
| FedSWABackbone=ResNet-18, Data Heterogeneity (Dirichlet coefficient)=0.6, Max Rounds=1000R, Target Accuracy=55%2025.07 | 59.8 | — | — | — | 574 | |
| FedACGBackbone=ResNet-18, Data Heterogeneity (Dirichlet coefficient)=0.3, Max Rounds=1000R, Target Accuracy=55%2025.07 | 57.7 | — | — | — | 717 | |
| MoFedSAMBackbone=ResNet-18, Data Heterogeneity (Dirichlet coefficient)=0.3, Max Rounds=1000R, Target Accuracy=55%2025.07 | 57.5 | — | — | — | 770 | |
| FedSWABackbone=ResNet-18, Data Heterogeneity (Dirichlet coefficient)=0.3, Max Rounds=1000R, Target Accuracy=55%2025.07 | 55.5 | — | — | — | 889 | |
| FedAvgBackbone=ResNet-18, Data Heterogeneity (Dirichlet coefficient)=0.6, Max Rounds=1000R, Target Accuracy=55%2025.07 | 54.2 | — | — | — | 1,000 | |
| FedLESAMBackbone=ResNet-18, Data Heterogeneity (Dirichlet coefficient)=0.3, Max Rounds=1000R, Target Accuracy=55%2025.07 | 53.3 | — | — | — | 1,000 | |
| QSGD[2]+FedBCGD+Backbone=LeNet-5, Heterogeneity (ρ)=0.6, Number of clients (N)=5, Total communication floats budget=200d2026.03 | 53.1 | — | — | 56 | — | |
| FedAvgBackbone=ResNet-18, Data Heterogeneity (Dirichlet coefficient)=0.3, Max Rounds=1000R, Target Accuracy=55%2025.07 | 52.5 | — | — | — | 1,000 | |
| SCAFFOLDBackbone=ResNet-18, Data Heterogeneity (Dirichlet coefficient)=0.6, Max Rounds=1000R, Target Accuracy=55%2025.07 | 52.3 | — | — | — | 1,000 | |
| QSGD[2]+FedBCGDBackbone=LeNet-5, Heterogeneity (ρ)=0.6, Number of clients (N)=5, Total communication floats budget=200d2026.03 | 52.2 | — | — | 61 | — | |
| FedACGBackbone=ResNet-18, Data Heterogeneity (Dirichlet coefficient)=0.1, Max Rounds=1000R, Target Accuracy=55%2025.07 | 52.2 | — | — | — | 1,000 | |
| FedLESAMBackbone=ResNet-18, Data Heterogeneity (Dirichlet coefficient)=0.6, Max Rounds=1000R, Target Accuracy=55%2025.07 | 52.1 | — | — | — | 1,000 | |
| FedSAMBackbone=ResNet-18, Data Heterogeneity (Dirichlet coefficient)=0.6, Max Rounds=1000R, Target Accuracy=55%2025.07 | 51.9 | — | — | — | 1,000 | |
| MoFedSAMBackbone=ResNet-18, Data Heterogeneity (Dirichlet coefficient)=0.1, Max Rounds=1000R, Target Accuracy=55%2025.07 | 51.5 | — | — | — | 1,000 | |
| SCAFFOLDBackbone=ResNet-18, Data Heterogeneity (Dirichlet coefficient)=0.3, Max Rounds=1000R, Target Accuracy=55%2025.07 | 50.3 | — | — | — | 1,000 | |
| FedSWABackbone=ResNet-18, Data Heterogeneity (Dirichlet coefficient)=0.1, Max Rounds=1000R, Target Accuracy=55%2025.07 | 50.3 | — | — | — | 1,000 | |
| FedASAMBackbone=ResNet-18, Data Heterogeneity (Dirichlet coefficient)=0.6, Max Rounds=1000R, Target Accuracy=55%2025.07 | 49.8 | — | — | — | 1,000 | |
| FedBCGD+Backbone=LeNet-5, Heterogeneity (ρ)=0.6, Number of clients (N)=5, Total communication floats budget=200d2026.03 | 49.6 | — | — | 89 | — | |
| FedSAMBackbone=ResNet-18, Data Heterogeneity (Dirichlet coefficient)=0.3, Max Rounds=1000R, Target Accuracy=55%2025.07 | 49 | — | — | — | 1,000 | |
| FedBCGDBackbone=LeNet-5, Heterogeneity (ρ)=0.6, Number of clients (N)=5, Total communication floats budget=200d2026.03 | 48.7 | — | — | 91 | — | |
| FedLESAMBackbone=ResNet-18, Data Heterogeneity (Dirichlet coefficient)=0.1, Max Rounds=1000R, Target Accuracy=55%2025.07 | 48.7 | — | — | — | 1,000 | |
| FedASAMBackbone=ResNet-18, Data Heterogeneity (Dirichlet coefficient)=0.1, Max Rounds=1000R, Target Accuracy=55%2025.07 | 47.7 | — | — | — | 1,000 | |
| FedASAMBackbone=ResNet-18, Data Heterogeneity (Dirichlet coefficient)=0.3, Max Rounds=1000R, Target Accuracy=55%2025.07 | 46.6 | — | — | — | 1,000 | |
| FedDynBackbone=ResNet-18, Data Heterogeneity (Dirichlet coefficient)=0.6, Max Rounds=1000R, Target Accuracy=55%2025.07 | 46.5 | — | — | — | 1,000 | |
| FedDynBackbone=ResNet-18, Data Heterogeneity (Dirichlet coefficient)=0.3, Max Rounds=1000R, Target Accuracy=55%2025.07 | 45.9 | — | — | — | 1,000 | |
| FedAvgBackbone=ResNet-18, Data Heterogeneity (Dirichlet coefficient)=0.1, Max Rounds=1000R, Target Accuracy=55%2025.07 | 45.8 | — | — | — | 1,000 | |
| FedDynBackbone=ResNet-18, Data Heterogeneity (Dirichlet coefficient)=0.1, Max Rounds=1000R, Target Accuracy=55%2025.07 | 45.8 | — | — | — | 1,000 | |
| SCAFFOLDBackbone=ResNet-18, Data Heterogeneity (Dirichlet coefficient)=0.1, Max Rounds=1000R, Target Accuracy=55%2025.07 | 44.3 | — | — | — | 1,000 | |
| FedPAQBackbone=LeNet-5, Heterogeneity (ρ)=0.6, Number of clients (N)=5, Total communication floats budget=200d2026.03 | 43.3 | — | — | 110 | — | |
| TOP-kBackbone=LeNet-5, Heterogeneity (ρ)=0.6, Number of clients (N)=5, Total communication floats budget=200d2026.03 | 42.2 | — | — | 112 | — | |
| FedSAMBackbone=ResNet-18, Data Heterogeneity (Dirichlet coefficient)=0.1, Max Rounds=1000R, Target Accuracy=55%2025.07 | 40.1 | — | — | — | 1,000 | |
| FedAvgBackbone=LeNet-5, Heterogeneity (ρ)=0.6, Number of clients (N)=5, Total communication floats budget=200d2026.03 | 35.4 | — | — | — | — | |
| FedAvgbeta=0.62022.03 | — | 81.67 | 563.67 | — | — | |
| FedDFbeta=0.62022.03 | — | 90 | 445 | — | — | |
| FedDynbeta=0.62022.03 | — | 56 | 213.67 | — | — | |
| FedFTGbeta=0.62022.03 | — | 55 | 152.33 | — | — | |
| FedGenbeta=0.62022.03 | — | 82 | 571.33 | — | — | |
| FedProxbeta=0.62022.03 | — | 81.67 | 476 | — | — | |
| MOONbeta=0.62022.03 | — | 83.67 | 354 | — | — | |
| SCAFFOLDbeta=0.62022.03 | — | 61.67 | 186.33 | — | — |