Federated Learning Personalization via User Segmentation
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Solution Overview
Problem
Existing deep learning structures for service prediction models are limited in personalization and privacy protection, as they rely on large-scale cloud data and do not effectively cater to individual user groups, leading to inefficient and non-private model training.
Innovation Solution
Implementing a method that classifies users into groups for personalized model generation through federated learning, allowing for the creation of multi-models that are optimized for each user group while maintaining privacy by using in-device data and updating models locally, without relying on central data storage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a single model is trained using large-scale cloud data, then the model can be commonly used by all users, but personalization and privacy protection are limited
Solution Approach 1:
The patent segments users into different groups based on their characteristics and trains separate prediction models for each group. Instead of using a single general model for all users, the system divides the user population into segments (e.g., based on demographics, behavior patterns, or preferences) and generates personalized models for each segment through federated learning, thereby improving personalization while maintaining privacy.
Solution Approach 2:
The patent introduces federated learning as an intermediary mechanism that enables model training without direct data sharing. The federated learning framework acts as a mediator between users' local devices and the central server, allowing models to be trained on distributed data while preventing raw data from leaving user devices, thus resolving the contradiction between personalization and privacy protection.
2Measurement precision
If a single model is trained on cloud data, then model training can be centralized, but it leads to unnecessary generalization and reduced accuracy for individual user groups
Solution Approach 1:
The patent applies segmentation by dividing the homogeneous training approach into heterogeneous group-specific training. Users are segmented into different groups based on their characteristics, and separate prediction models are trained for each group. This segmentation allows each model to be optimized for its specific user group's patterns and preferences, improving prediction accuracy for individual users while avoiding the unnecessary generalization that occurs with a single universal model.
3Productivity
If user data is collected and stored centrally, then model training can be performed comprehensively, but the risk of data leakage increases
Solution Approach 1:
The patent uses federated learning as an intermediary framework that eliminates the need for central data storage while enabling comprehensive model training. The federated learning system mediates between local user devices and the central server, allowing model parameters to be aggregated without transferring raw user data. This approach maintains model training efficiency by leveraging distributed computing resources while eliminating the data leakage risk associated with centralized data storage.
Solution Approach 2:
The patent implements self-service by enabling each user's device to independently train and contribute to the prediction model using their own local data. Users' devices perform local model training and send only model updates (not raw data) to the server. This self-service approach allows comprehensive model training across all users while keeping data processing distributed and secure, thereby maintaining productivity while eliminating data leakage risks.
Data Source
AI summary
Disclosed is a method, system, and non-transitory computer-readable record medium for providing a multi-model through federated learning using personalization. The method includes classifying users into a plurality of groups; and generating a prediction model for a service as a multi-model through federated learning for each of the plurality of groups.