FedTSC: A Secure Federated Learning System for Interpretable Time Series Classification

Summary: FedTSC extends federated learning to secure, interpretable time series classification (TSC), balancing security, interpretability, accuracy, and efficiency. Three explainability-driven TSC methods, optimized communication protocols, and a Sklearn-like Python API for practical deployment. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 13051
Venue: VLDB
Year: 2022
Pagerank: 5.9425753e-05
Overall Rank: 6,212 | 57.39%
DOI: 10.14778/3554821.3554875

Incoming Non-self Citations Over Time

Authors

1. Zhiyu Liang (Harbin Engineering University)
2. Hongzhi Wang (Harbin Engineering University)

BibTeX Citation

@article{liang_vldb22,
        title = {{FedTSC: A Secure Federated Learning System for Interpretable Time Series Classification}},
        author = {Liang, Zhiyu and Wang, Hongzhi},
        journal = {PVLDB},
        series = {{VLDB} '22},
        volume = {15},
        number = {12},
        pages = {3686--3689},
        doi = {10.14778/3554821.3554875},
        url = {https://doi.org/10.14778/3554821.3554875},
        year = {2022}
}

Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
10,883	TEAM: Topological Evolution-aware Framework for Traffic Forecasting	2025	VLDB	5.093636e-05

Outgoing Citations (Sorted by Pagerank)

Showing 2 of 2 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank	Cited Paper	Year	Venue	Pagerank
1,249	Privacy Preserving Vertical Federated Learning for Tree-based Models	2020	VLDB	0.00011495357
1,959	VF2Boost: Very Fast Vertical Federated Gradient Boosting for Cross-Enterprise Learning	2021	SIGMOD	9.4090198e-05

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