Fig. 2: Overview of the proposed federated learning approach. | Nature Communications

Fig. 2: Overview of the proposed federated learning approach.

From: Privacy-preserving collaborative battery fault warning for massive electric vehicles by heterogeneous data from charging stations

Fig. 2: Overview of the proposed federated learning approach.

The top part represents the central server, which uses a hyper-model to generate customized weight parameters for each local model. The bottom part is numerous data owners, where each data owner possesses some charging stations and a local fault warning model. The flags of 1–5 refer to five steps: ① (blue wide arrow in the cloud) initiating local models, and generating their model parameters through a hyper-model by data owner embedding vectors; ② (green narrow arrows from top to bottom) distributing the model parameters to each data owner through wireless communication; ③ (self-loop arrows with multiple colors) implementing local training to update the parameters of local model only by the data of this owner; ④ (green narrow lines from bottom to top) delivering the changes of parameters of the local model to the central server; ⑤ (purple narrow arrow in the cloud) updating the parameters of hyper-model according to the gradient of the received parameter changes of local model.

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