The rapid expansion of Federated Learning (FL) within Edge AI has enabled decentralized networks to collaboratively train sophisticated models without sharing raw, privacy-sensitive data [8]. However, this strict adherence to data privacy creates a significant "blind spot" for AI governance. Under emerging regulatory frameworks like the EU AI Act, regulators require proof of data safety, bias mitigation, and privacy preservation [4]. The current paradigm forces a paradox: regulators cannot verify whether individual edge nodes complied with these laws without violating the very privacy FL is designed to protect. In this paper, we propose a novel Zero-Knowledge Governance (ZKG) framework. By integrating Zero-Knowledge Succinct Non-Interactive Arguments of Knowledge (zk-SNARKs) into the local edge training phase, edge devices can cryptographically prove that their local datasets and model updates comply with encoded governance policies, without ever revealing the underlying data [1]. We present the architectural design of ZKG, analyze the vulnerabilities it addresses in decentralized AI, and propose optimizations to make cryptographic auditing viable for resource-constrained edge hardware.
Chidiebere Christopher· Zenodo (CERN European Organi...· 0 citations
The rapid expansion of Federated Learning (FL) within Edge AI has enabled decentralized networks to collaboratively train sophisticated models without sharing raw, privacy-sensitive data [8]. However, this strict adherence to data privacy creates a significant "blind spot" for AI governance. Under emerging regulatory frameworks like the EU AI Act, regulators require proof of data safety, bias mitigation, and privacy preservation [4]. The current paradigm forces a paradox: regulators cannot verify whether individual edge nodes complied with these laws without violating the very privacy FL is designed to protect. In this paper, we propose a novel Zero-Knowledge Governance (ZKG) framework. By integrating Zero-Knowledge Succinct Non-Interactive Arguments of Knowledge (zk-SNARKs) into the local edge training phase, edge devices can cryptographically prove that their local datasets and model updates comply with encoded governance policies, without ever revealing the underlying data [1]. We present the architectural design of ZKG, analyze the vulnerabilities it addresses in decentralized AI, and propose optimizations to make cryptographic auditing viable for resource-constrained edge hardware.
Chidiebere Christopher· Zenodo (CERN European Organi...· 0 citations