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An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
Distributed robust estimation and inference with contaminated data
Peiliang Zhang1, Wen-Xin Zhou2, Zhao Ren1
1Department of Statistics, University of Pittsburgh, Pittsburgh, PA, 15260, USA.
Electronic Journal of Statistics
|August 6, 2026
Summary
This study introduces robust methods for distributed learning with partially contaminated data. The new framework handles widespread data corruption efficiently, ensuring reliable estimation and inference.
Area of Science:
- Machine Learning
- Distributed Systems
- Statistics
Background:
- Traditional robust methods often assume limited, localized data contamination (e.g., Byzantine failures).
- Real-world distributed systems frequently encounter partial contamination, where all nodes may have corrupted data.
- Existing approaches are insufficient for scenarios with widespread, partial data corruption across all machines.
Purpose of the Study:
- To develop a robust framework for distributed learning systems facing partial data contamination.
- To enable accurate estimation and inference even when all machines' datasets contain corrupted observations.
- To generalize Huber's epsilon-contamination model to distributed settings.
Main Methods:
- Generalizing Huber's epsilon-contamination model for distributed partial contamination.
- Developing a communication-efficient M-estimator for linear models in distributed settings.
- Introducing a distributed multiplier bootstrap for robust inference without extra communication.
- Proposing a debiasing procedure to address bias in high contamination scenarios.
Main Results:
- The proposed M-estimator achieves optimal convergence rates, matching centralized data performance.
- The distributed multiplier bootstrap provides efficient robust inference post-estimation.
- The methods demonstrate robustness and scalability across various contamination levels.
- The debiasing procedure effectively mitigates bias caused by high contamination proportions.
Conclusions:
- The developed framework offers a robust solution for distributed learning with partial data contamination.
- The methods provide efficient and scalable estimation and inference capabilities.
- This work advances the robustness of machine learning in practical, real-world distributed environments.
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