提出加密数据共享的联邦学习框架，加速边缘设备模型收敛。
Efficient Federated Learning with Encrypted Data Sharing for Data-Heterogeneous Edge Devices
- 用随机层训练数据加密器，实现安全数据共享
- 融合本地与加密共享数据，提升训练效率
- 适合对收敛速度要求高的边缘部署场景
随着隐私保护重要性提升，越来越多模型在边缘设备上通过联邦学习（FL）训练并上传至中心服务器。然而现有研究忽视了网络拓扑、物理距离和数据异构性对边缘设备的影响，导致延迟增加和模型性能下降。为此，我们提出一种新型边缘联邦学习方案——带加密数据共享的联邦学习（FedEDS）。FedEDS利用客户端模型及其随机层训练数据加密器，生成加密数据并与其他客户端共享。客户端使用对应随机层与加密数据联合训练本地模型。该方法结合本地私有数据与来自其他客户端的加密共享数据进行训练，显著加快联邦学习收敛速度，并缓解数据异构带来的负面影响，适用于需快速收敛的边缘部署应用。实验结果验证了FedEDS在提升模型性能方面的有效性。
原文摘要 · Abstract (English)
As privacy protection gains increasing importance, more models are being trained on edge devices and subsequently merged into the central server through Federated Learning (FL). However, current research overlooks the impact of network topology, physical distance, and data heterogeneity on edge devices, leading to issues such as increased latency and degraded model performance. To address these issues, we propose a new federated learning scheme on edge devices that called Federated Learning with Encrypted Data Sharing(FedEDS). FedEDS uses the client model and the model's stochastic layer to train the data encryptor. The data encryptor generates encrypted data and shares it with other clients. The client uses the corresponding client's stochastic layer and encrypted data to train and adjust the local model. FedEDS uses the client's local private data and encrypted shared data from other clients to train the model. This approach accelerates the convergence speed of federated learning training and mitigates the negative impact of data heterogeneity, making it suitable for application services deployed on edge devices requiring rapid convergence. Experiments results show the efficacy of FedEDS in promoting model performance.
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