Data Acquisition via Experimental Design for Decentralized Data Markets

Core Concepts

Optimizing data selection in decentralized markets through experimental design improves prediction accuracy without labeled validation data.

Abstract

Abstract: Acquiring high-quality training data is crucial for machine learning models, especially in data-scarce domains like healthcare. Data markets incentivize data sharing, but selecting valuable data points remains a challenge. Introduction: Data valuation techniques are inadequate for decentralized markets. A federated approach based on experimental design enhances prediction accuracy without labeled validation data. Challenges with Current Data Valuation Approaches: Existing methods overfit when the dimensionality is high or the validation set is small, highlighting the limitations of current approaches. Methods: The proposed linear experimental design optimizes data selection directly using unlabeled test data, outperforming traditional valuation methods. Fast and Federated Optimization: The Frank-Wolfe algorithm efficiently updates weights iteratively, making the method scalable and suitable for decentralized markets. Experiments: The method demonstrates superior performance on various real-world datasets compared to existing valuation techniques. Ablation Experiments: Varying factors like the number of test points and regularization strength impact performance, suggesting avenues for further research.

Stats

Unlike prior work, our method achieves lower prediction error without requiring labeled validation data. Our approach directly estimates the benefit of acquiring data for test set prediction in a decentralized market setting.

Quotes

"Our proposed method maintains low test error as more seller training data is selected." "Attributing the influence of training data to validation performance is not equivalent to estimating data value for predicting unseen test data."

Key Insights Distilled From

Data Acquisition via Experimental Design for Decentralized Data Markets

by Charles Lu,B... at arxiv.org 03-22-2024

https://arxiv.org/pdf/2403.13893.pdf

Deeper Inquiries

How can differential privacy techniques be integrated into this method to enhance privacy guarantees

To enhance privacy guarantees in this method, differential privacy techniques can be integrated into the federated optimization process. By adding noise to the gradients computed by each seller before sharing them with the central platform, differential privacy ensures that individual data points cannot be reverse-engineered from the aggregated information. This way, sensitive information is protected while still allowing for collaborative model training across decentralized data sources.

What are the implications of applying local steps like FedAvg or SCAFFOLD to decrease communication costs

Applying local steps like FedAvg or SCAFFOLD to decrease communication costs would involve updating the weights and models locally at each seller's end before aggregating them at a central server. This approach reduces the amount of information that needs to be exchanged between sellers and the platform, making it more communication-efficient. By leveraging these techniques, sellers can perform computations on their own data without revealing raw information, thereby minimizing communication overhead in a decentralized setting.

How does the scalability of this method compare when applied to different types of datasets

The scalability of this method varies depending on the type of dataset being used. For high-dimensional datasets with large amounts of data points (such as medical imaging datasets), the method may require more computational resources and time due to increased complexity. On simpler datasets with fewer dimensions and data points (like synthetic Gaussian datasets), the method may exhibit faster computation times and better performance due to lower dimensionality constraints. Therefore, when applied to different types of datasets, scalability considerations should take into account factors such as dataset size, dimensionality, and computational requirements for optimal performance.