SEAL: An Open, Auditable, and Fair Data Generation Framework for AI-Native 6G Networks

TL;DR

SEAL is a comprehensive framework for generating synthetic data in AI-native 6G networks, emphasizing ethics, fairness, auditability, and privacy to support responsible AI development.

Contribution

It introduces the SEAL framework with ERCD and FL modules, enhancing data quality, fairness, and regulatory compliance in synthetic data generation for 6G.

Findings

SEAL outperforms existing methods in Frechet Inception Distance.

SEAL improves fairness metrics such as equalized odds.

SEAL achieves higher accuracy in synthetic data generation.

Abstract

AI-native 6G networks promise to transform the telecom industry by enabling dynamic resource allocation, predictive maintenance, and ultra-reliable low-latency communications across all layers, which are essential for applications such as smart cities, autonomous vehicles, and immersive XR. However, the deployment of 6G systems results in severe data scarcity, hindering the training of efficient AI models. Synthetic data generation is extensively used to fill this gap; however, it introduces challenges related to dataset bias, auditability, and compliance with regulatory frameworks. In this regard, we propose the Synthetic Data Generation with Ethics Audit Loop (SEAL) framework, which extends baseline modular pipelines with an Ethical and Regulatory Compliance by Design (ERCD) module and a Federated Learning (FL) feedback system. The ERCD integrates fairness, bias detection, and standardized audit trails for regulatory mapping, while the FL enables privacy-preserving calibration using aggregated insights from real testbeds to close the reality-simulation gap. Results show that the SEAL framework outperforms existing methods in terms of Frechet Inception Distance, equalized odds, and accuracy. These results validate the framework's ability to generate auditable and bias-mitigated synthetic data for responsible AI-native 6G development.