Federated learning for time series forecasting enables clients with privacy-sensitive time series data to collaboratively learn accurate forecasting models, e.g., in energy load prediction.
Unfortunately, privacy risks in federated learning persist, as servers can potentially reconstruct clients' training data through gradient inversion attacks.
While gradient inversion attacks are demonstrated for image, text and tabular classification tasks, little is known for time series regression tasks.
In this paper, we first conduct an extensive empirical study on inverting time series data across 4 time series forecasting models and 4 datasets, identifying the unique challenges of reconstructing both observations and targets of time series data.
We then propose TS-Inverse, a novel gradient inversion attack that improves the inversion of time series data through (i) learning a gradient inversion model that outputs quantile predictions, (ii) a unique loss function incorporating periodicity and trend regularization, and (iii) regularization according to the quantile predictions. Our evaluations demonstrate a remarkable performance of TS-Inverse, achieving at least 2x-10x improvement in terms of sMAPE metric over existing gradient inversion attacks methods on time series data.

Machine learning models are increasingly being used in fields that have a direct impact on the lives of humans. Often these machine learning models are black-box models and they lack transparency and trust which is holding back the implementation. To increase transparency and trust this research investigates whether imitation learning, specifically Generative Adversarial ImitationLearning (GAIL), can be used to give insights into the black-box models by extracting decision trees. To achieve this, an extension of GAIL was made allowing it to extract decision trees. The decision trees were then measured in terms of performance, fidelity, behavior, and interpretability in three different environments. We find that GAIL is able to extract decision trees with high fidelity and can give insightful information into the expert models. Moreover, further research can be done on more complex environments and black-box models, other surrogate models, and possibilities for more specific local insights.