The need of data owners for privacy protection has given rise to collaborative learning, and data-related issues heterogeneity faced by federated learning has further given rise to clustered federated learning; whereas the traditional privacy-preserving scheme of federated learning using homomorphic encryption alone fails to fulfill the privacy protection demands of clustered federated learning. To address these issues, this research provides an effective and safeguarded answer for sharing homomorphic encryption keys among clusters in clustered federated learning grounded in conditional representative broadcast re-encryption. This method constructs a key sharing mechanism. By combining the functions of the bilinear pairwise accumulator and specific conditional proxy broadcast re-ciphering, the mechanism can verify the integrity of homomorphic encryption keys stored on cloud servers. In addition, the solution enables key management centers to grant secure and controlled access to re-encrypted homomorphic encryption keys to third parties without disclosing the sensitive information contained therein. The scheme achieves this by implementing a sophisticated access tree-based mechanism that enables the cloud server to convert forwarded ciphertexts into completely new ciphertexts customized specifically for a given group of users. By effectively utilizing conditional restrictions, the scheme achieves fine-grained access control to protect the privacy of shared content. Finally, this paper showcases the scheme’s security against selective ciphertext attacks without relying on random prediction.