Secure aggregation of user vectors has become a critical issue in the field of federated learning. Many Secure Aggregation Protocols (SAP) face exorbitant computation costs, which severely limit their applicability. We uncover that current endeavors to reduce computation costs tend to overlook a crucial fact: a considerable portion of SAP's computation burden stems from processing each entry in the private vectors. Given this observation, we propose PVF, a portable module for compressing computation costs. PVF is able to ``freeze'' a substantial portion of the private vector through specific linear transformations, only requiring $\frac{1}{\lambda}$ of the original vector to participate in SAP. Eventually, users can ``thaw'' the public sum of the ``frozen entries" by the result of SAP. To enhance functionality, we introduce extensions that can enforce consistency constraints on users' original vectors, verify aggregated results, and enhance security when a portion of the private vector is known to the server. We demonstrate that PVF can seamlessly integrate with various SAP and prove that it poses no threat to user privacy in the semi-honest and active adversary settings. We select $8$ baselines, encompassing $6$ distinct types of SAP, and explore the acceleration effects of PVF on these SAP. Empirical investigations indicate that when $\lambda=100$, PVF yields up to $99.5\times$ speedup and up to $32.3\times$ communication reduction, with the potential to approach nearly $1000\times$ acceleration as $\lambda$ increases.

翻译：用户向量的安全聚合已成为联邦学习领域中的关键问题。许多安全聚合协议面临高昂的计算成本，这严重限制了其适用性。我们发现，当前旨在降低计算成本的努力往往忽略了一个关键事实：安全聚合协议计算负担的相当一部分源于对私有向量中每个条目的处理。基于这一观察，我们提出了PVF，一种用于压缩计算成本的可移植模块。PVF能够通过特定的线性变换“冻结”私有向量的很大一部分，仅需原始向量的$\frac{1}{\lambda}$参与安全聚合协议。最终，用户可以通过安全聚合协议的结果“解冻”“冻结条目”的公共和。为增强功能，我们引入了扩展，可以对用户的原始向量施加一致性约束、验证聚合结果，并在服务器已知部分私有向量的情况下提升安全性。我们证明PVF可以无缝集成到各种安全聚合协议中，并证明其在半诚实和主动敌手设置下不会威胁用户隐私。我们选取了涵盖6种不同类型安全聚合协议的8个基线，并研究了PVF对这些安全聚合协议的加速效果。实证研究表明，当$\lambda=100$时，PVF可实现高达$99.5\times$的加速和$32.3\times$的通信量减少，且随着$\lambda$的增加，有可能接近$1000\times$的加速效果。