CertiRank: Aligning Federated Rank Learning with Secure and Certified Aggregation

Privacy-preserving Byzantine-robust Federated Learning (PBFL) aims to protect client updates while mitigating poisoning attacks. While homomorphic encryption (HE) has become a mainstream approach for PBFL, the combination of real-valued updates, robust aggregation, and encrypted computation often incurs substantial overhead. To address this challenge, we propose CertiRank, a rank-based PBFL that aligns federated rank learning with secure and certified aggregation. By leveraging integer-valued ranking updates and lightweight additive aggregation, CertiRank eliminates costly real-to-integer conversion and avoids complex ciphertext-domain similarity computations. To strengthen robustness against malicious clients, we introduce Certified Membership-gated Ranking Aggregation (CMGRA), which decouples subnetwork selection from fine-grained rank ordering and certifies membership changes using honest-support evidence. To securely realize CMGRA, we develop a customized secure aggregation mechanism that combines SEncode, RLWE-AHE, and privacy-preserving validity verification, enabling efficient additive aggregation and certification directly over encrypted ranking updates. Experiments demonstrate that CertiRank achieves strong robustness and competitive model utility while substantially reducing computation and communication overhead compared with existing HE-based PBFL schemes. Our code is available at https://anonymous.4open.science/r/CertiRank.