Trustworthy federated learning aims to achieve optimal performance while ensuring clients' privacy. Existing privacy-preserving federated learning approaches are mostly tailored for image data, lacking applications for time series data, which have many important applications, like machine health monitoring, human activity recognition, etc. Furthermore, protective noising on a time series data analytics model can significantly interfere with temporal-dependent learning, leading to a greater decline in accuracy. To address these issues, we develop a privacy-preserving federated learning algorithm for time series data. Specifically, we employ local differential privacy to extend the privacy protection trust boundary to the clients. We also incorporate shuffle techniques to achieve a privacy amplification, mitigating the accuracy decline caused by leveraging local differential privacy. Extensive experiments were conducted on five time series datasets. The evaluation results reveal that our algorithm experienced minimal accuracy loss compared to non-private federated learning in both small and large client scenarios. Under the same level of privacy protection, our algorithm demonstrated improved accuracy compared to the centralized differentially private federated learning in both scenarios.

翻译：可信联邦学习旨在保障客户隐私的同时实现最优性能。现有隐私保护联邦学习方法主要针对图像数据设计，缺乏对时间序列数据的应用——而时序数据在机器健康监测、人类活动识别等领域具有重要应用价值。此外，在时间序列数据分析模型上施加保护性噪声会显著干扰时序依赖性学习，导致准确率严重下降。为解决这些问题，我们提出了一种面向时间序列数据的隐私保护联邦学习算法。具体而言，采用本地差分隐私将隐私保护信任边界扩展至客户端，同时结合分组技术实现隐私放大，缓解因使用本地差分隐私导致的准确率下降。基于五个时间序列数据集的实验表明，在客户端规模较小和较大两种场景下，与无隐私保护的联邦学习相比，本算法仅产生极小的准确率损失。在相同隐私保护水平下，本算法在两种场景中均展现出比中心化差分隐私联邦学习更优的准确率。