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Found 7 papers, 0 papers with code
Estimation of conditional average treatment effects on distributed data: A privacy-preserving approach
Second, our method enables collaborative estimation between different parties as well as multiple time points because the dimensionality-reduced intermediate representations can be accumulated.
Non-readily identifiable data collaboration analysis for multiple datasets including personal information
This study then proposes a non-readily identifiable DC analysis only sharing non-readily identifiable data for multiple medical datasets including personal information.
Another Use of SMOTE for Interpretable Data Collaboration Analysis
DC analysis centralizes individually constructed dimensionality-reduced intermediate representations and realizes integrated analysis via collaboration representations without sharing the original data.
Collaborative causal inference on distributed data
Many existing methods for distributed data focus on resolving the lack of subjects (samples) and can only reduce random errors in estimating treatment effects.
Application of Particle Swarm Optimization method to On-going Monitoring for estimating vehicle-bridge interaction system
Using the particle swarm optimization (PSO) method, the vehicle and bridge parameters and the road unevenness can be estimated by updating the parameters to minimize the objective function.
Federated Learning System without Model Sharing through Integration of Dimensional Reduced Data Representations
Dimensionality Reduction is a commonly used element in a machine learning pipeline that helps to extract important features from high-dimensional data.
Interpretable collaborative data analysis on distributed data
This paper proposes an interpretable non-model sharing collaborative data analysis method as one of the federated learning systems, which is an emerging technology to analyze distributed data.
