FedST: Secure Federated Shapelet Transformation for Time Series Classification

Abstract

This paper explores how to build a shapelet-based time series classification (TSC) model in the federated learning (FL) scenario, that is, using more data from multiple owners without actually sharing the data. We propose FedST, a novel federated TSC framework extended from a centralized shapelet transformation method. We recognize the federated shapelet search step as the kernel of FedST. Thus, we design a basic protocol for the FedST kernel that we prove to be secure and accurate. However, we identify that the basic protocol suffers from efficiency bottlenecks and the centralized acceleration techniques lose their efficacy due to the security issues. To speed up the federated protocol with security guarantee, we propose several optimizations tailored for the FL setting. Our theoretical analysis shows that the proposed methods are secure and more efficient. We conduct extensive experiments using both synthetic and real-world datasets. Empirical results show that our FedST solution is effective in terms of TSC accuracy, and the proposed optimizations can achieve three orders of magnitude of speedup.

References in corpus (14)

• Deep Learning with Differential Privacy
• Federated Learning: Challenges, Methods, and Future Directions
• Deep learning for time series classification: a review
• Secure Federated Transfer Learning
• Benchmarking Multivariate Time Series Classification Algorithms
• MINIROCKET: A Very Fast (Almost) Deterministic Transform for Time Series Classification
• Privacy Preserving Vertical Federated Learning for Tree-based Models
• The Canonical Interval Forest (CIF) Classifier for Time Series Classification
• Scalable Dictionary Classifiers for Time Series Classification
• The Temporal Dictionary Ensemble (TDE) Classifier for Time Series Classification
• From BOP to BOSS and Beyond: Time Series Classification with Dictionary Based Classifiers
• Spatio-Temporal Neural Networks for Space-Time Series Forecasting and Relations Discovery
• BlindFL: Vertical Federated Machine Learning without Peeking into Your Data
• Large-Scale Secure XGB for Vertical Federated Learning