Fig. 3: Performance to address data heterogeneity.
From: A framework reforming personalized Internet of Things by federated meta-learning
For each sub-figure in a–c, it illustrates the accuracy distribution of a model trained by the compared methods, namely FedAvg, FedMeta, FedFomaml, FedReptile and Cedar, in 5 individual heterogeneity levels manipulated by α = [0.1, 0.5, 1.0, 5.0, 10.0]. In each box, the blue line represents the median, the red triangle represents the mean, the upper and lower edges of the box represent the upper and lower quartiles, the whiskers indicate 1.5 times the interquartile range, and the circles represent outliers. Horizontally, each set of three sub-figures represents the results for FMNIST (a), FER (b), and ISIC (c), respectively. Vertically, the three sub-figures with yellow, blue, and green backgrounds correspond to the results for MobileNetV2, ResNet18, and DenseNet121, respectively. Finally, d show the average accuracy achieved by the three models over the above three datasets with α = 0.1 in 5 overall heterogeneity levels controlled by ς = [0.2, 0.4, 0.6, 0.8, 1], i.e., MobileNetV2 in yellow background, ResNet18 in blue background, and DenseNet121 in green background. Specific results for each dataset are presented in the Supplementary Information (SI Section 4.5).
