Research Article
Local Epochs Inefficiency Caused by Device Heterogeneity in Federated Learning
Table 7
Weights model vs. baseline.
| Datasets | Model | Test RMSE | Test MAPE | Validation RMSE | Validation MAPE |
| P100_Conv | Baseline | 2.962 ms | 14.36% | 3.455 ms | 15.17% | Weights model | 2.894 ms | 15.24% | 3.091 ms | 14.58% | V100_Conv | Baseline | 1.722 ms | 11.44% | 1.547 ms | 11.13% | Weights model | 1.660 ms | 11.03% | 1.850 ms | 11.49% | K40_Conv | Baseline | 10.136 ms | 15.59% | 10.361 ms | 16.39% | Weights model | 9.051 ms | 15.14% | 11.675 ms | 15.16% | P100_Dense | Baseline | 0.033 ms | 3.15% | 0.032 ms | 3.04% | Weights model | 0.032 ms | 3.05% | 0.032 ms | 3.02% | V100_Dense | Baseline | 0.051 ms | 6.26% | 0.046 ms | 5.84% | Weights model | 0.046 ms | 6.07% | 0.047 ms | 6.10% | K40_Dense | Baseline | 0.157 ms | 7.57% | 0.179 ms | 7.92% | Weights model | 0.159 ms | 7.11% | 0.150 ms | 7.07% | All_Conv | Baseline | 4.079 ms | 11.44% | 4.021 ms | 11.16% | Weights model | 4.273 ms | 10.40% | 3.893 ms | 10.39% | All_Dense | Baseline | 0.077 ms | 4.70% | 0.074 ms | 4.68% | Weights model | 0.077 ms | 4.72% | 0.076 ms | 4.73% |
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