DP-Fed6G: An adaptive differential privacy-empowered federated learning framework for 6G networks^☆

Abstract

The advent of 6G networks is poised to drive a new era of intelligent, privacy-preserving distributed learning by leveraging advanced communication and AI-driven edge intelligence. Federated Learning (FL) has emerged as a promising paradigm to enable collaborative model training without exposing raw data. However, its deployment in 6G networks faces significant obstacles, including vulnerabilities to inference attacks, the complexities of heterogeneous and dynamic network environments, and the inherent trade-off between privacy protection and model performance. In response to these challenges, we introduce DP-Fed6G, a novel FL framework that integrates differential privacy (DP) to fortify data security while ensuring high-quality learning outcomes. Specifically, DP-Fed6G employs an adaptive noise injection strategy that dynamically adjusts privacy protection levels based on real-time 6G network conditions and device heterogeneity, ensuring robust data security while maximizing model performance and optimizing the trade-off between privacy and utility. Extensive experiments on three real-world healthcare datasets demonstrate that DP-Fed6G consistently outperforms existing baselines (DP-FedSGD and DP-FedAvg), achieving up to 10.3% higher test accuracy under the same privacy budget. The proposed framework thus provides a practical solution for secure and privacy-preserving AI in 6G, supporting intelligent decision-making in privacy-sensitive applications.

Keywords

Differential privacy / Federated learning / 6G / Gaussian noise

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