Measurement data

as a crucial operational element for all stakeholders in distribution network management and a cornerstone asset for enterprises

exhibit diverse characteristics such as varying privacy protection requirements among stakeholders and heterogeneous forms of datasets. These characteristics significantly constrain the empowerment potential of measurement data. Federated learning has garnered widespread attention for its ability to address data silo issues. However

traditional federated learning frameworks are plagued by inadequate privacy protection for participant data

decreased model performance due to data heterogeneity

and a lack of effective incentive mechanisms. To tackle these issues

the adaptive privacy-protected dynamic federated learning framework (AP-DFL) is proposed. First

considering the different emphasis of privacy protection for different load types

the sensitivity of the dataset is defined from the two-dimensional perspectives of anonymity and confidentiality. On this basis

the privacy budget for each round of training on the edge side is dynamically adjusted to achieve adaptive local differential perturbation. Then

combined with the global differential perturbation on the main station side

privacy attacks are effectively avoided. Next

a participant contribution assessment model based on matrix decomposition Shapley values is proposed. This model efficiently calculates contribution values through the reconstructed method of value matrix decomposition under sampling. The aggregation weights are adaptively adjusted based on the contribution values to achieve dynamic federated aggregation

thus enhancing the convergence speed of the model under data heterogeneity. Finally

experimental analysis is conducted on this federated learning framework in typical distribution network scenarios

demonstrating its feasibility.