Federated Learning Model Integration for Privacy-Safe Customer Prediction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Business operators face challenges in utilizing customer data for marketing due to the inability to share personal information, hindering effective cross-business analysis and predictive modeling.
Innovation Solution
A federated learning model generation apparatus and method that integrates local learning models from multiple business operators to generate a federated learning model, allowing for the prediction of customer behavior without sharing personal information, using techniques like federated learning to combine data structures across local models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If business operators share customer data to improve marketing analysis, then marketing effectiveness is improved, but data privacy protection deteriorates
Solution Approach 1:
The patent segments customer data into local learning models distributed across different business operators. Each operator maintains their own local model trained on their specific customer data, preventing centralized data consolidation while enabling cross-business analysis through model federation. This segmentation resolves the contradiction by allowing marketing effectiveness improvement without compromising data privacy.
Solution Approach 2:
The patent introduces a federated learning framework as an intermediary between business operators. Instead of directly sharing customer data, operators train local learning models independently and share only model parameters through the federation mechanism. This intermediary layer enables collaborative marketing analysis while maintaining data privacy protection, as the actual customer data never leaves the operators' controlled environments.
2Adaptability or versatility
If personal information is shared among business operators to enable cross-business analysis, then analysis capability is improved, but information security deteriorates
Solution Approach 1:
The patent segments personal information into distributed local learning models, where each business operator maintains control over their own data. The segmentation allows cross-business analysis capability through model federation while preserving information security, as no single entity gains access to aggregated personal information from multiple operators.
Solution Approach 2:
The patent creates copies of learning models at each business operator's location rather than sharing original data. Each operator maintains a local copy of the learning model trained on their specific data, enabling analysis capabilities through model exchange while protecting information security by preventing direct data sharing. The federated learning system synchronizes these copies without transferring underlying personal information.
3Measurement precision
If local learning models are integrated to generate federated learning model, then predictive accuracy is improved, but system complexity increases
Solution Approach 1:
The patent merges multiple local learning models into a unified federated learning model through parameter aggregation. This combining approach improves predictive accuracy by integrating insights from different business operators while managing system complexity through standardized federation protocols that automate the integration process.
Solution Approach 2:
The patent designs the federated learning system with universal components that can accommodate multiple business operators and data types. The standardized model architecture and federation mechanisms provide multi-functionality, allowing the system to handle diverse prediction tasks across different industries while reducing overall system complexity through reusable components.
Data Source
AI summary
Provided is a federated learning model, a generation apparatus thereof, and the like that easily and suitably contribute to marketing activities. A federated learning model generation apparatus includes a local learning model acquisition unit and a federated learning model generation unit. The local learning model acquisition unit acquires a plurality of different local learning models that have learned a relationship between a plurality of customer groups respectively generated from business customer data owned by a plurality of business operators and consumption behaviors corresponding to the business operators. The federated learning model generation unit receives a predetermined consumption behavior of a customer as input data by federating at least a part of the acquired local learning models, and generates a federated learning model that outputs prospective customer data for the input data.