DIST-FL: Enhancing Security for TEE-based Aggregation in Federated Learning

Computer Science > Cryptography and Security [Submitted on 3 Jun 2026] DIST-FL: Enhancing Security for TEE-based Aggregation in Federated Learning Guanlong Wu, Ju Yang, Zhen Huang, Jianyu Niu, Guoxing Chen, Jianzong Wang, Yinqian Zhang Trusted Execution Environments (TEEs)-aided federated learning protocols emerge as promising solutions to counter server-side adversaries and ensure the trustworthiness of the server. In this paper, we dissect existing protocols and demonstrate that server-side adversaries can still manipulate client selection and replay aggregation to compromise system robustness and privacy, by exploiting TEE limitations, i.e., state rollback and I/O manipulation. To this end, we present DIST-FL, a distributed system of servers guarded by multiple TEEs forming an append-only ledger for privacy-preserved, robust FL aggregation. Specifically, DIST-FL ensures operation linearizability to thwart state rollback attacks and incorporates inputs from reliable servers to mitigate I/O manipulation threats. We implement DIST-FL and conduct evaluations in WAN settings. Experimental results demonstrate that DIST-FL can effectively counter the proposed attacks and match the single-TEE's performance while offering a 6x throughput boost over its counterparts, leveraging TEE's computational advantages. Subjects: Cryptography and Security (cs.CR) Cite as: arXiv:2606.04899 [cs.CR]   (or arXiv:2606.04899v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2606.04899 Focus to learn more Submission history From: Ju Yang [view email] [v1] Wed, 3 Jun 2026 14:01:10 UTC (1,299 KB) Access Paper: HTML (experimental) view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-06 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?)