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. 2024 Oct 19;15:9044. doi: 10.1038/s41467-024-53352-9

Table 1.

Performance of different methods on BDG2 and CBTs in terms of accuracy, memory, training time, and communication overhead per round

Method BDG2 CBTs Memory (KB) Training Time (s) Communication (KB)
RMSE MAPE MAE RMSE MAPE MAE
Cen 22.82 8.41 8.20 0.4780 26.98 0.2727 2578.42 (1.0 ×) 586.42 (2.05 ×)
Local-S 23.56 8.13 8.12 0.4698 28.19 0.2726 152.53 (16.9 ×) 41.19 (29.14 ×)
FedAvg-S 22.79 7.67 7.98 0.4681 27.04 0.2699 152.53 (16.9 ×) 46.49 (28.80 ×) 5.50 (27.18 ×)
FedProx-S 22.58 7.71 7.91 0.4668 27.17 0.2678 152.53 (16.9 ×) 46.49 (28.80 ×) 5.50 (27.18 ×)
Local-M 22.84 7.75 8.03 0.4645 26.67 0.2682 2578.42 (1.0 ×) 1187.15 (1.01 ×)
FedAvg-M 22.25 6.96 7.48 0.4614 25.81 0.2637 2578.42 (1.0 ×) 1200.44 (1.0 ×) 149.50 (1.0 ×)
FedProx-M 22.21 7.16 7.62 0.4622 25.76 0.2639 2578.42 (1.0 ×) 1200.44 (1.0 ×) 149.50 (1.0 ×)
Split 23.27 7.68 7.96 0.4679 26.83 0.2687 103.75 (24.8 ×) 96.00 (12.50 ×) 91.25 (1.63 ×)
SFLV1 22.34 7.25 7.68 0.4647 26.03 0.2664 103.75 (24.8 ×) 96.49 (12.44 ×) 96.75 (1.54 ×)
SFLV2 22.76 7.53 7.89 0.4674 26.49 0.2683 103.75 (24.8 ×) 96.49 (12.44 ×) 96.75 (1.54 ×)
Proposed 22.17 6.98 7.44 0.4630 25.74 0.2636 115.07 (22.4 ×) 62.41 (19.23 ×) 74.43 (2.01 ×)

1The best-performing and second-best-performing methods are bolded and underlined, respectively.

2-S and -M indicates that the model has single and multiple hidden layers, respectively.