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Trustworthy federated learning aims to achieve optimal performance whileensuring clients' privacy. Existing privacy-preserving federated learningapproaches are mostly tailored for image data, lacking applications for timeseries data, which have many important applications, like machine healthmonitoring, human activity recognition, etc. Furthermore, protective noising ona time series data analytics model can significantly interfere withtemporal-dependent learning, leading to a greater decline in accuracy. Toaddress these issues, we develop a privacy-preserving federated learningalgorithm for time series data. Specifically, we employ local differentialprivacy to extend the privacy protection trust boundary to the clients. We alsoincorporate shuffle techniques to achieve a privacy amplification, mitigatingthe accuracy decline caused by leveraging local differential privacy. Extensiveexperiments were conducted on five time series datasets. The evaluation resultsreveal that our algorithm experienced minimal accuracy loss compared tonon-private federated learning in both small and large client scenarios. Underthe same level of privacy protection, our algorithm demonstrated improvedaccuracy compared to the centralized differentially private federated learningin both scenarios.

Shuffled Differentially Private Federated Learning for Time Series Data Analytics