Price-based demand response cannot live without accurate characterization of user response, but these data mostly exist in the interaction between the demand response aggregator and users. Because of the imperative of privacy protection, the data often remains confidential, manifesting as isolated data islands. In response to the current problem where user data privacy requirements clash with the demands of grid scheduling, this paper proposes a method for characterizing user electricity price response behavior based on federated learning and its application. Initially, a distributed interactive framework for characterizing user electricity price response behavior through federated learning is established, transforming raw data information into feature-based interactions. Subsequently, a differential privacy-federated proximal algorithm is employed to aggregate parameters from various utility providers’ electricity price response models, resulting in a regional user electricity price response model. Finally, an application method for optimizing the operation of the distribution network is presented by embedding the response model. An improved strategy proximal optimization algorithm is used to determine real-time electricity prices and energy storage output. Case studies confirm that the proposed approach accurately characterizes regional user electricity price response behavior while preserving the privacy of user energy consumption information and enhancing the overall efficacy of the distribution network.