Federated Model Performance Evaluation With Randomized Response
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Solution Overview
Problem
In federated learning environments, there is a challenge in evaluating the performance of machine learning models while protecting the privacy of local labeled data, as existing methods fail to efficiently determine performance indicators without exposing sensitive ground-truth labels.
Innovation Solution
A method is introduced where client nodes modify ground-truth labels using a randomized response mechanism to generate protected labels, determining error metric information based on these labels, and send this information to a server node, which then corrects the error values to assess model performance without revealing the original labels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If ground-truth labels are used directly for model performance evaluation, then measurement precision is improved, but data privacy protection deteriorates
Solution Approach 1:
The patent introduces protected labels as an intermediary between ground-truth labels and the model performance evaluation process. These protected labels are generated through a randomized response mechanism that adds noise to the original labels, thereby preventing direct exposure of sensitive ground-truth data while still enabling performance metric calculation. The intermediary layer maintains evaluation functionality while protecting privacy.
Solution Approach 2:
The patent transforms the ground-truth labels into protected labels by changing their parameter values through a randomized response mechanism. This mechanism modifies the label values with certain probabilities, creating a transformed version that preserves statistical properties needed for evaluation while obscuring the original sensitive information. The parameter transformation enables privacy protection without completely losing evaluation capability.
2Object-affected harmful factors
If protected labels are used for model performance evaluation, then data privacy protection is improved, but measurement precision deteriorates
Solution Approach 1:
The patent implements a feedback mechanism where the server node receives protected labels from multiple client nodes, calculates performance indicators based on these protected labels, and then provides feedback corrections to compensate for the noise introduced during label protection. This feedback loop allows the system to iteratively improve measurement precision while maintaining privacy protection, as the corrections help reverse the distortion caused by randomization.
Solution Approach 2:
The patent combines protected labels from multiple client nodes to aggregate performance evaluation data. By merging data from multiple sources, the system leverages the law of large numbers to reduce the impact of individual randomizations, thereby improving overall measurement precision. The aggregation process allows privacy protection at the individual level while achieving accurate population-level performance metrics.
3Productivity
If ground-truth labels are exposed for performance evaluation, then productivity is improved, but data privacy protection deteriorates
Solution Approach 1:
The patent introduces protected labels as an intermediary that enables efficient performance evaluation without requiring direct exposure of ground-truth labels. This intermediary mechanism allows the evaluation process to proceed with modified labels that preserve necessary statistical properties while preventing privacy leakage. The approach maintains productivity by avoiding the need for complex secure multi-party computation or other slow cryptographic protocols.
Solution Approach 2:
The patent implements a self-service mechanism where each client node independently generates its own protected labels using a randomized response mechanism, without requiring interaction with other nodes or a trusted third party for label protection. This self-service approach maintains high evaluation efficiency by allowing parallel processing of protected labels from multiple clients while inherently protecting each node's private data through local randomization.
Data Source
AI summary
According to embodiments of the disclosure, methods, apparatuses, devices, and medium for model performance evaluation are provided. The method comprises: obtaining, at a client node, a plurality of predicted scores output by a machine learning model for a plurality of data samples, the plurality of predicted scores respectively indicating predicted probabilities that the plurality of data samples belong to a first category or a second category; modifying a plurality of ground-truth labels based on a randomized response mechanism, to obtain a plurality of protected labels, the plurality of ground-truth labels respectively labeling that the plurality of data samples belong to the first category or the second category; determining error metric information related to a predetermined performance indicator of the machine learning model based on the plurality of protected labels and the plurality of predicted scores; and sending the error metric information to a server node. In this way, while a model performance evaluation is implemented, the purpose of privacy protection for local labeled data of a client node is achieved.