Federated learning, a pioneering paradigm, enables collaborative model training without exposing users' data to central servers.

Several methods have emerged to aggregate diverse client models; however, they either lack the ability of personalization, raise privacy and security concerns, need prior knowledge, or ignore the capability and functionality of personalized models.

Federated learning, a pioneering paradigm, enables collaborative model training without exposing users' data to central servers. Several methods have emerged to aggregate diverse client models; however, they either lack the ability of personalization, raise privacy and security concerns, need prior knowledge, or ignore the capability and functionality of personalized models. In this paper, we present an innovative approach, named pFedClub, which addresses these challenges. Initially, pFedClub dissects heterogeneous client models into blocks and organizes them into functional groups on the server. Utilizing the designed CMSR (Controllable Model Searching and Reproduction) algorithm, pFedClub generates a range of personalized candidate models for each client.