面向智能体语义通信模型更新的特征重建辅助联邦学习中的差分隐私
Differential Privacy in Feature Reconstruction Aided Federated Learning for Agent's Semantic Communication Model Update
AI总结：
本文提出差分隐私辅助的FedSFR框架，通过特征传输与模型选择机制，在异构无线环境下提升语义通信模型训练的通信效率、稳定性与图像重建质量，且隐私保护效果优于基于梯度的传输方案。
AI中文摘要：
本文提出一种基于语义特征重建联邦学习算法（FedSFR）的差分隐私联邦学习（FL）框架，用于训练图像传输的语义通信模块。该算法允许上行链路容量不佳的客户端传输从本地训练的联合信源信道编码（JSCC）编码器提取的低维语义特征向量，从而在异构无线条件下提升通信效率与训练稳定性。为保护客户端隐私，本文引入一次性拉普拉斯机制，从理论上证明在相同通信预算下，基于特征的传输比基于梯度的传输能实现更严格的差分隐私（DP）保障；此外，还引入模型选择机制以缓解隐私保护扰动导致的性能下降。在多个数据集上的实验结果表明，所提出的差分隐私辅助FedSFR在异构无线系统中的训练稳定性与图像重建质量均优于启用差分隐私的FedAvg。
英文摘要：
This paper proposes a differentially private federated learning (FL) framework built upon an FL algorithm with semantic feature reconstruction (FedSFR) for training semantic communication modules for image transmission. By allowing clients with unfavorable uplink capacity to transmit low-dimensional semantic feature vectors extracted from locally trained joint source-channel coding (JSCC) encoders, FedSFR enhances communication efficiency and training stability under heterogeneous wireless conditions. To protect client privacy, we incorporate the oneshot Laplace mechanism and theoretically demonstrate that feature-based transmission achieves strictly stronger differential privacy (DP) guarantees than gradient-based transmission under an identical communication budget. In addition, a model selection mechanism is introduced to alleviate performance degradation caused by privacy-preserving perturbations. Experimental results on multiple datasets show that the proposed DP-aided FedSFR outperforms DP-enabled FedAvg in training stability and image reconstruction quality in heterogeneous wireless systems.
