  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.

Machine Learning;Artificial Intelligence;Cryptography and Security