Research Article
Local Epochs Inefficiency Caused by Device Heterogeneity in Federated Learning
Table 8
The standard deviation of convolutional feature weights ranking.
| Features | P100_Conv | V100_Conv | K40_Conv | MeanRankStd |
| Batchsize | 1.7653 | 1.3045 | 1.4026 | 1.4908 | Elements_matrix | 2.3233 | 2.0775 | 1.1578 | 1.8529 | Elements_kernel | 1.7904 | 2.2949 | 1.4356 | 1.8403 | Channels_in | 0.4440 | 0.4218 | 0.7599 | 0.5419 | Channels_out | 3.0549 | 2.1915 | 2.1091 | 2.4518 | Padding | 1.6192 | 1.8036 | 1.2830 | 1.5686 | Strides | 2.4347 | 2.0012 | 3.2603 | 2.5654 | Use_bias | 1.2580 | 1.5514 | 1.3097 | 1.3730 | Opt_SGD | 2.3229 | 3.0940 | 1.6340 | 2.3503 | Opt_Adadelta | 2.2164 | 2.8144 | 1.9606 | 2.3305 | Opt_Adagrad | 2.7459 | 2.4789 | 1.1959 | 2.1402 | Opt_Momentum | 2.6090 | 2.1952 | 1.1699 | 1.9914 | Opt_Adam | 1.4019 | 2.1550 | 2.4311 | 1.9960 | Opt_RMSProp | 2.0723 | 2.3218 | 0.9707 | 1.7883 | Act_relu | 1.5234 | 1.0455 | 0.9251 | 1.1647 | Act_tanh | 1.7225 | 1.1762 | 1.4122 | 1.4370 | Act_sigmoid | 1.3042 | 1.1182 | 1.1107 | 1.1777 |
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