Title: GuardHFL: Privacy Guardian for Heterogeneous Federated Learning. Abstract: Heterogeneous federated learning (HFL) enables clients with different computation and communication capabilities to collaboratively train their own customized models via a query-response paradigm on auxiliary datasets. However, such paradigm raises serious privacy issues due to the leakage of highly sensitive query samples and response predictions. Although existing secure querying solutions may be extended to enhance the privacy of HFL with non-trivial adaptation, they suffer from two key limitations: (1) lacking customized protocol designs and (2) relying on heavy cryptographic primitives, which could lead to poor performance. In this work, we put forth GuardHFL, the first-of-its-kind efficient and privacy-preserving HFL framework. GuardHFL is equipped with a novel HFL-friendly secure querying scheme that is built on lightweight secret sharing and symmetric-key techniques. Its core is a set of customized multiplication and comparison protocols, which substantially boost the execution efficiency. Extensive evaluations demonstrate that GuardHFL outperforms the state-of-the-art works by up to two orders of magnitude in efficiency.