LAPA-based Dynamic Privacy Optimization for Wireless Federated Learning in Heterogeneous Environments

TL;DR

This paper introduces a novel LAPA-based approach for dynamic privacy optimization in wireless federated learning, balancing privacy and utility in heterogeneous, Non-IID environments through adaptive noise allocation and transmission power control.

Contribution

It proposes a lightweight adaptive privacy allocation strategy combined with DDPG-based power optimization to improve federated learning performance while ensuring privacy in heterogeneous settings.

Findings

Enhanced convergence performance with personalized noise allocation.

Effective balance between privacy and system utility achieved.

Improved aggregation accuracy in Non-IID data scenarios.

Abstract

Federated Learning (FL) is a distributed machine learning paradigm based on protecting data privacy of devices, which however, can still be broken by gradient leakage attack via parameter inversion techniques. Differential privacy (DP) technology reduces the risk of private data leakage by adding artificial noise to the gradients, but detrimental to the FL utility at the same time, especially in the scenario where the data is Non-Independent Identically Distributed (Non-IID). Based on the impact of heterogeneous data on aggregation performance, this paper proposes a Lightweight Adaptive Privacy Allocation (LAPA) strategy, which assigns personalized privacy budgets to devices in each aggregation round without transmitting any additional information beyond gradients, ensuring both privacy protection and aggregation efficiency. Furthermore, the Deep Deterministic Policy Gradient (DDPG) algorithm is employed to optimize the transmission power, in order to determine the optimal timing at which the adaptively attenuated artificial noise aligns with the communication noise, enabling an effective balance between DP and system utility. Finally, a reliable aggregation strategy is designed by integrating communication quality and data distribution characteristics, which improves aggregation performance while preserving privacy. Experimental results demonstrate that the personalized noise allocation and dynamic optimization strategy based on LAPA proposed in this paper enhances convergence performance while satisfying the privacy requirements of FL.