FedEDAuth -- Federated Embedding Distribution Authentication for Counterfeit IC Detection

TL;DR

FedEDAuth is a lightweight federated authentication framework that detects malicious clients in counterfeit IC detection, significantly improving security and accuracy in collaborative semiconductor supply chain models.

Contribution

The paper introduces FedEDAuth, a novel embedding-level client authentication method that effectively identifies poisoned clients without accessing raw data, enhancing federated learning security.

Findings

Achieved 100% detection of poisoned clients in experiments with 50 participants.

Secured 94.17% accuracy in counterfeit IC classification after filtering malicious clients.

Validated FedEDAuth's effectiveness in real-world federated learning scenarios.

Abstract

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.