Framework analysis demonstrates cryptographic verification of AI governance policies in federated edge networks, indicating verifiable regulatory compliance without exposing private data.
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.
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Chidiebere Christopher (2026) studied this question.