FedEDAuth -- Federated Embedding Distribution Authentication for Counterfeit IC Detection

Computer Science > Cryptography and Security [Submitted on 15 May 2026] FedEDAuth -- Federated Embedding Distribution Authentication for Counterfeit IC Detection Naseeruddin Lodge, Dhruva Aklekar, Vineet Chadalavada, Nahush Tambe, Sina Gholami, Minhaj Alam, Fareena Saqib The widespread of counterfeit integrated circuits (ICs) poses severe risks to the security, reliability, and trustworthiness of modern electronic systems. Federated learning (FL) offers a privacy-preserving paradigm for collaborative counterfeit detection across the semiconductor supply chain, but its vulnerability to byzantine data poisoning attacks limits practical deployment. This paper presents Federated Embedding Distribution Authentication (FedEDAuth), a lightweight, embedding level client authentication framework that detects and filters malicious participants before model aggregation. FedEDAuth leverages reference embedding distributions derived from a golden dataset and evaluates clients using outlier analysis, mean shift measurements, and micro-cluster behavior without requiring access to raw data or gradients. Integrated into standard FL pipelines, FedEDAuth consistently identifies all poisoned clients in experimental settings with 50 distributed participants under the byzantine data poisoning attack, achieving a 100% malicious client detection rate. After filtering, the federated model achieved a high counterfeit IC classification performance of 94.17% accuracy. These results not only validate FedEDAuth's effectiveness but also underscore the broader potential of secure, trustworthy FL frameworks as a critical advancement for next generation hardware security solutions, enabling robust, collaborative intelligence across the semiconductor supply chain. Comments: 9 pages, 6 figures, 2 tables Subjects: Cryptography and Security (cs.CR) Cite as: arXiv:2605.15885 [cs.CR]   (or arXiv:2605.15885v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2605.15885 Focus to learn more Submission history From: Naseeruddin Lodge [view email] [v1] Fri, 15 May 2026 12:08:01 UTC (814 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?)