Extended Data Fig. 3: Supplementary Figure 7. The reliability of the 4-client federated learning.
In memristor-based federated learning, errors occur during the memristor-based encryption and decryption computations with a computational error rate of \(x\). The error rate of memristor computation leads to an error rate in the weight gradient \(\Delta W\) for weight updating. For an individual user in federated learning, the encryption and decryption computations increase the error rate of \(\Delta W\) to \({1-(1-x)}^{2}\). For a 4-client federated learning process, the total error rate of \(\Delta W\) will theoretically reach \({1-(1-x)}^{8}\). In our experiment, the computational error rate (\(x\)) is measured to be 2.24%, and the theoretical total error rate of \(\Delta W\) is 17.7%. a-f, The simulated network performance under the total error rate of \(\Delta W\). The simulation experiment was repeated 50 times for each test point using different random seeds. In the figure, the red curve indicates the average of the 50 experiment results. The bounds of the box indicate the interquartile range, and the whiskers extend to the maximum and minimum non-outlier values within 1.5 times the interquartile range. MCC, Matthews Correlation Coefficient; ROC, Receiver Operating Characteristic.
