Privacy-Preserving Federated Radio Map Learning for Wireless Digital Twins via Adaptive Noise Allocation

TL;DR

This paper introduces an adaptive noise allocation method for federated radio map learning that enhances privacy protection while preserving reconstruction quality by dynamically distributing perturbation based on architecture-specific leakage.

Contribution

It proposes a novel, architecture-aware, adaptive noise allocation mechanism that improves privacy in federated radio map learning without sacrificing accuracy.

Findings

Adaptive noise allocation outperforms uniform methods in privacy protection.

The method maintains high reconstruction quality under a fixed perturbation budget.

Dynamic adjustment of noise scales improves transmitter privacy.

Abstract

Radio maps provide a foundational data layer for wireless digital twins, and federated learning offers a natural framework for their distributed construction without centralizing raw radio environment data. However, the exchanged client model updates may still leak transmitter-location information, even when the underlying measurement data are never shared. Existing noise-based privacy defenses inject perturbation either uniformly across all uploaded coordinates or according to a fixed static rule, thereby ignoring the architecture-specific structure of this leakage. This paper proposes a budget-constrained adaptive noise allocation mechanism that redistributes a fixed perturbation budget across transmitter-sensitive upload groups identified from the two-stage RadioUNet architecture. The proposed method uses low-dimensional upload statistics to dynamically adjust group-wise noise scales and is integrated locally before client upload transmission. We evaluate the framework on a federated radio map learning task under a unified noise multiplier, comparing it against uniform and structure-aware baselines using reconstruction mean squared error and transmitter localization error as metrics. Results show that adaptive allocation achieves the strongest privacy protection while maintaining the best reconstruction quality among all noise-based defenses under a matched perturbation budget.