Scalable and Privacy-Aware Online Learning of Nonlinear Structural Equation Models
Money, Rohan Thekkemarickal; Krishnan, Joshin Parakkulangarayil; Beferull-Lozano, Baltasar; Isufi, Elvin
Peer reviewed, Journal article
Published version
Permanent lenke
https://hdl.handle.net/11250/3128296Utgivelsesdato
2023Metadata
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Originalversjon
Money, R. T., Krishnan, J. P., Beferull-Lozano, B. & Isufi, E. (2023). Scalable and Privacy-Aware Online Learning of Nonlinear Structural Equation Models. IEEE Open Journal of Signal Processing, 4, s. 61-70. https://doi.org/10.1109/OJSP.2023.3241580Sammendrag
An online topology estimation algorithm for nonlinear structural equation models (SEM) is proposed in this paper, addressing the nonlinearity and the non-stationarity of real-world systems. The nonlinearity is modeled using kernel formulations, and the curse of dimensionality associated with the kernels is mitigated using random feature approximation. The online learning strategy uses a group-lasso-based optimization framework with a prediction-corrections technique that accounts for the model evolution. The proposed approach has three properties of interest. First, it enjoys node-separable learning, which allows for scalability in large networks. Second, it offers privacy in SEM learning by replacing the actual data with node-specific random features. Third, its performance can be characterized theoretically via a dynamic regret analysis, showing that it is possible to obtain a linear dynamic regret bound under mild assumptions. Numerical results with synthetic and real data corroborate our findings and show competitive performance w.r.t. state-of-the-art alternatives.