mlsanityData to 14 Oct 2026

FedEDAuth -- Federated Embedding Distribution Authentication for Counterfeit IC Detection

arXiv 2605.15885 ↗First posted 15 May 2026cs.CRcited by 0

On the map: LLM safety and security → Federated unlearning and poisoning

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Federated unlearning and poisoning, one of 12 directions in LLM safety and security. Builds on 11 of its 29 references that are in the map. First posted on arXiv 15 May 2026.

Builds on

11 of 29 references.

References · 11

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2018How To Backdoor Federated Learningcited by 765
  3. 2018Byzantine-Robust Distributed Learning: Towards Optimal Statistical Ratescited by 711
  4. 2018Federated Learning for Mobile Keyboard Predictioncited by 572
  5. 2019Local Model Poisoning Attacks to Byzantine-Robust Federated Learningcited by 493
  6. 2019Fall of Empires: Breaking Byzantine-tolerant SGD by Inner Product Manipulationcited by 141
  7. 2020Certified Robustness to Label-Flipping Attacks via Randomized Smoothingcited by 108
  8. 2018Label Sanitization against Label Flipping Poisoning Attackscited by 73
  9. 2021Challenges and Approaches for Mitigating Byzantine Attacks in Federated Learningcited by 47
  10. 2023Federated Learning for Connected and Automated Vehicles: A Survey of Existing Approaches and Challengescited by 40
  11. 2020Learning Assisted Side Channel Delay Test for Detection of Recycled ICscited by 2

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  1. 2026DECIFR: Domain-Aware Exfiltration of Circuit Information from Federated Gradient Reconstruction ↗
  2. 2026A Data-Free Membership Inference Attack on Federated Learning in Hardware Assurance
  3. 2022FedCC: Robust Federated Learning against Model Poisoning Attacks
  4. 2023FedCIP: Federated Client Intellectual Property Protection with Traitor Tracking
  5. 2022FedTracker: Furnishing Ownership Verification and Traceability for Federated Learning Model
  6. 2026FedSurrogate: Backdoor Defense in Federated Learning via Layer Criticality and Surrogate Replacement
  7. 2026Backdoors Leave Structural Traces: FedMAST for Backdoor Detection and Containment in Federated Learning
  8. 2023FLTracer: Accurate Poisoning Attack Provenance in Federated Learning