Federated Learning Coordination for Privacy-Preserving Customer Targeting
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Solution Overview
Problem
Business operators face challenges in sharing customer data for marketing purposes due to privacy concerns, limiting the effectiveness of data utilization across multiple entities.
Innovation Solution
A federated learning model is generated by integrating local learning models from different business operators, allowing for the analysis of customer data without sharing personal information, and outputting prospective customer data for targeted marketing activities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If customer data is shared among multiple business operators, then marketing effectiveness and data utilization are improved, but privacy protection deteriorates
Solution Approach 1:
A federated learning server acts as an intermediary that coordinates multiple business operators without directly handling their customer data. The server distributes learning tasks to local models, collects aggregated gradients, and updates global models, enabling collaborative marketing analytics while keeping sensitive customer data localized and protected.
Solution Approach 2:
The system segments the centralized learning process into distributed local learning models at each business operator and a coordinating federated learning server. This segmentation allows each operator to maintain control over their own data while contributing to collective marketing insights through coordinated model training.
2Loss of information
If local learning models are federated across multiple business operators, then data utilization and marketing insights are improved, but system complexity increases
Solution Approach 1:
The federated learning server performs multiple functions: distributing learning tasks, collecting local model updates, aggregating gradients, and managing version control. This multi-functionality consolidates complex coordination tasks into a single universal platform, reducing overall system complexity despite the distributed architecture.
Solution Approach 2:
The system implements feedback loops where local learning models send performance metrics and gradient updates to the federated learning server, which then adjusts the global model and redistributes learning tasks. This automated feedback mechanism simplifies coordination by replacing manual intervention with systematic iterative optimization.
3Measurement precision
If personal information is collected for marketing analysis, then marketing precision is improved, but privacy protection deteriorates
Solution Approach 1:
The system extracts only the necessary gradient updates and model parameter changes from local learning processes, leaving the actual personal information and customer data at each business operator. This extraction approach enables collaborative learning while removing sensitive data from the federated transmission process.
Solution Approach 2:
Instead of sharing original customer data, the system creates and shares copies in the form of aggregated gradient information and model updates. These copies contain statistical patterns and insights necessary for marketing precision while being mathematically transformed to exclude identifiable personal information.
Data Source
AI summary
Provided is a data processing apparatus and the like that easily and suitably contribute to marketing activities. In a data processing apparatus, an input unit receives input data regarding a predetermined consumption behavior. A federated learning model is generated such that at least a part of local learning models generated for a plurality of different business operators are federated. The federated learning model described above is generated by learning a relationship between a plurality of customer groups respectively generated from business customer data owned by the business operators and consumption behaviors corresponding to the business operators. The federated learning model is set to be able to output prospective customer data with respect to the input data. An output unit outputs the prospective customer data.