Privacy concerns can significantly impact the scalability and adoption of FedImpro in real-world applications. The need to protect sensitive data while estimating shared feature distributions adds an extra layer of complexity to the implementation process. Ensuring privacy compliance may require additional measures such as secure communication protocols or encryption techniques, which could increase computational overhead and affect system performance. Moreover, strict privacy regulations or user concerns about data security could limit the willingness of organizations or individuals to participate in federated learning scenarios using FedImpro's approach.