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

Towards Efficient Secure Aggregation in FL: Partial Vector Freezing for Cost Compression