Leveraging Gradient-Based Metrics for Selective Data Sharing and Valuation in Private Decentralized Machine Learning

Khái niệm cốt lõi

Gradient-based metrics like Variance of Gradients (VoG) and Privacy Loss-Input Susceptibility (PLIS) can be used to identify valuable training data samples and incentivize data sharing in private decentralized machine learning settings.

Tóm tắt

The paper investigates how gradient-based metrics can be leveraged to identify valuable training data samples in private decentralized machine learning settings, where regulatory concerns and lack of data owner incentives pose challenges.
Key insights:

VoG and PLIS scores can effectively identify atypical and informative data samples that are beneficial for model generalization, even in strict privacy settings with differential privacy (DP).

VoG-based sample selection is more consistent across different model architectures, datasets, and privacy regimes compared to commonly used metrics like per-sample loss and gradient norms.

As model size increases, identifying valuable samples becomes more challenging, but VoG and PLIS can still provide useful guidance.

The relationship between PLIS (identifying privacy-sensitive samples) and VoG (identifying difficult samples) is complex, suggesting the need for further investigation into what makes a sample informative.

Differentially private versions of VoG and PLIS can be used to share these sensitive metrics with participants, enabling incentivization and selective data sharing in private federated learning.

The authors demonstrate the potential of gradient-based metrics to address the dual challenges of privacy and incentivization in collaborative machine learning settings.

Thống kê

"Obtaining high-quality data for collaborative training of machine learning models can be a challenging task due to A) regulatory concerns and B) a lack of data owner incentives to participate."
"DP noise can adversely affect the underrepresented and the atypical (yet often informative) data samples, making it difficult to assess their usefulness."
"We show that these techniques can provide the federated clients with tools for principled data selection even in stricter privacy settings."

Trích dẫn

"Firstly, a number of data protection and governance regulations (such as GDPR) stipulate that the collection and usage of sensitive data should be minimised."
"Currently there is no general agreement on how to establish how much individual data samples are worth and the model owners do not have the incentive to pay for them unless their model is likely to improve from it."

Thông tin chi tiết chính được chắt lọc từ

Incentivising the federation: gradient-based metrics for data selection and valuation in private decentralised training

by Dmitrii Usyn... lúc arxiv.org 04-17-2024

https://arxiv.org/pdf/2305.02942.pdf

Yêu cầu sâu hơn

How can the relationship between PLIS (identifying privacy-sensitive samples) and VoG (identifying difficult samples) be further explored to better understand what makes a sample informative for model training

To further explore the relationship between PLIS and VoG in understanding what makes a sample informative for model training, a detailed analysis of the characteristics of samples identified by each metric is essential. One approach could involve conducting a comparative study on a diverse set of datasets and model architectures to observe how samples with high PLIS and VoG values contribute to model performance. By analyzing the features of these samples, such as image complexity, rarity, and relevance to the task, we can gain insights into the intersection between privacy sensitivity and sample difficulty.
Additionally, conducting qualitative assessments by human annotators to evaluate the relevance and informativeness of samples identified by PLIS and VoG could provide valuable insights. By correlating these human assessments with the metric values, we can validate the effectiveness of PLIS and VoG in selecting informative samples for model training. This approach can help in understanding the nuances of sample selection based on privacy sensitivity and difficulty, leading to a more comprehensive understanding of what makes a sample valuable for model training.

What are the potential drawbacks or limitations of using differentially private versions of VoG and PLIS for data valuation and incentivization in federated learning, and how can these be addressed

Using differentially private versions of VoG and PLIS for data valuation and incentivization in federated learning may have certain drawbacks and limitations that need to be addressed. One potential limitation is the trade-off between privacy guarantees and the utility of the selected samples. The noise introduced in the differential privacy mechanism can impact the accuracy of VoG and PLIS values, leading to suboptimal sample selection. To address this, advanced privacy-preserving techniques, such as adaptive privacy budgets or tailored noise addition strategies, can be explored to balance privacy and utility effectively.
Another drawback is the computational overhead associated with calculating differentially private VoG and PLIS values, which can impact the scalability of the approach in large-scale federated learning settings. Optimizing the computation of these metrics through parallel processing or efficient algorithms can help mitigate this limitation. Additionally, ensuring transparency and accountability in the incentivization process by incorporating fairness and bias mitigation techniques can enhance the trust and participation of data owners in the federated learning framework.
To address these limitations, continuous research and development efforts are needed to refine the differential privacy mechanisms, optimize the computation of privacy-sensitive metrics, and integrate fairness considerations into the data valuation and incentivization process. Collaborative efforts between researchers, industry stakeholders, and regulatory bodies can drive innovation in privacy-preserving federated learning practices and address the challenges associated with using differential privacy for data valuation.

How can the insights from this work on gradient-based data selection be extended to other domains beyond computer vision, such as natural language processing or healthcare applications

The insights from gradient-based data selection in computer vision can be extended to other domains, such as natural language processing (NLP) and healthcare applications, by adapting the methodology to suit the specific characteristics of these domains. In NLP, for example, gradient-based metrics can be used to identify informative text samples for training language models or sentiment analysis algorithms. By analyzing the gradients of word embeddings or sentence representations, valuable data points can be selected to improve model performance.
In healthcare applications, gradient-based data selection can be applied to medical imaging datasets for tasks like disease diagnosis or treatment planning. By leveraging VoG and PLIS metrics on medical images, healthcare providers can identify challenging cases or privacy-sensitive data points that are crucial for training accurate and robust AI models. This approach can enhance the quality of healthcare AI systems while ensuring patient privacy and data security.
Furthermore, the principles of data valuation and incentivization in federated learning can be extended to various industries beyond computer vision, NLP, and healthcare. Fields such as finance, manufacturing, and retail can benefit from using gradient-based metrics to optimize data selection, improve model performance, and incentivize data sharing among distributed parties. By customizing the methodology to suit the specific requirements of each domain, the insights from gradient-based data selection can drive innovation and collaboration in a wide range of applications.