Federated learning trains models across multiple devices using decentralized data, without exchanging actual data. However, standard federated learning approaches face limitations in terms of high communication and computational costs, personalization, and vulnerability to attacks. To address these challenges, we propose a novel partial sharing algorithm. The partial sharing algorithm trains local models by dividing them into personalized and shared parts. This allows clients to generate personalized models that are optimized for individual client data while exposing only the shared portion of the local model to potential attacks. Through experiments, we evaluate the personalization and robustness of our proposed algorithm and demonstrate its superior performance compared to existing approaches.