Joint Learning Model Weight Transfer via Clustering
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Solution Overview
Problem
In many application scenarios, obtaining label data for fine-tuning global models in joint learning is challenging due to the difficulty in accessing label data, which hinders the effectiveness of the method.
Innovation Solution
A method that clusters non-label data to determine clustering center points, calculates weights for label data based on these centers and non-label data, and constructs a joint learning model to execute business tasks without requiring explicit label data from the target user.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If fine-tuning global model using label data of target user, then model accuracy for target user is improved, but label data of target user is difficult to obtain
Solution Approach 1:
The patent introduces clustering center points as intermediaries between non-label data and label data. These center points are derived from clustering non-label data and serve as a bridge to transfer information from joint users' label data to the target user's model, enabling fine-tuning without direct access to target user's label data
Solution Approach 2:
The patent creates virtual label data by transferring weights from joint users' label data through clustering center points. This copying mechanism allows the target user to obtain synthetic label information that mimics the statistical properties of true label data, enabling model fine-tuning without requiring actual label data
2Adaptability or versatility
If transfer non-label data to label data through weight determination, then business task execution is enabled, but data security and privacy must be maintained
Solution Approach 1:
The clustering center points act as privacy-preserving intermediaries that allow information transfer without exposing raw data. The centers are computed from non-label data and used to transfer statistical information from joint users, enabling task execution while maintaining data security through the intermediary representation
Solution Approach 2:
The patent replaces direct data access and sharing mechanisms with a weight transfer mechanism based on clustering. Instead of mechanically sharing or accessing raw label data, the system uses mathematical transformations (clustering and weight calculation) to transfer information, substituting physical data movement with computational abstraction
Data Source
AI summary
A service task execution method and apparatus, and a computer-readable storage medium and an electronic device. The method comprises: clustering a plurality of pieces of non-label data corresponding to a service task of a target user, so as to determine at least two cluster center points (101); according to the at least two cluster center points and the plurality of pieces of non-label data, determining weights corresponding to a plurality of pieces of label data of a joint user, wherein the plurality of pieces of label data correspond to the service task (102); and according to the plurality of pieces of label data of each joint user and the weights corresponding to the plurality of pieces of label data, constructing a joint learning model, wherein the joint learning model is used for executing the service task of the target user (103).