Verifiable Secure Aggregation via Dual Servers with Linear Tags in Federated Learning

Computer Science > Cryptography and Security [Submitted on 22 May 2026] Verifiable Secure Aggregation via Dual Servers with Linear Tags in Federated Learning Yufei Zhou Federated learning (FL) enables collaborative model training by aggregating local updates without requiring raw data sharing. However, prior studies have shown that servers can exploit gradient inversion to compromise user privacy or manipulate aggregation results, undermining the utility of the global model. To address these concerns, we propose a secure and verifiable aggregation scheme with lightweight cryptographic primitives for FL. Our method leverages pseudo-random functions (PRFs) and a non-colluding dual-server architecture to achieve secure aggregation with mutual server verification, while maintaining communication overhead comparable to plaintext aggregation and a constant verification tag size. Crucially, it preserves user privacy and achieves end-to-end secure aggregation with verification. Moreover, our scheme significantly reduces both user computation and verification overhead, making it suitable for FL with a large number of participants. For instance, with an input dimension of 20K, user computation time is reduced to 18 ms, approximately 7\times faster than OPSA, while verification time decreases to 9.5 ms, approximately 2.4\times faster than OPSA. Subjects: Cryptography and Security (cs.CR) Cite as: arXiv:2605.24054 [cs.CR]   (or arXiv:2605.24054v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2605.24054 Focus to learn more Submission history From: Yufei Zhou [view email] [v1] Fri, 22 May 2026 00:36:24 UTC (338 KB) Access Paper: HTML (experimental) view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-05 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)