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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
12863
Venue
VLDB
Year
2022
Pagerank
6.2885419e-05
Overall Rank
4,290 | 70.16%
DOI
10.14778/3554821.3554875

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Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank Citing Paper Year Venue Pagerank
10,629 TEAM: Topological Evolution-aware Framework for Traffic Forecasting 2025 VLDB 4.1945683e-05
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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
1,143 Privacy Preserving Vertical Federated Learning for Tree-based Models 2020 VLDB 0.00013710269
1,895 VF2Boost: Very Fast Vertical Federated Gradient Boosting for Cross-Enterprise Learning 2021 SIGMOD 0.00010180896
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