Model Selection via Condition Filtering in Federated Learning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current information processing systems face challenges in efficiently selecting and specifying suitable models for tasks based on user-defined conditions, particularly in federated learning environments where data privacy and security are paramount.
Innovation Solution
An information processing apparatus and method that receives condition information from a terminal device, determines which models satisfy these conditions, and transmits model specifying information to the device, allowing for the selection of appropriate models generated through learning processing, while ensuring data privacy and security through federated learning techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple models are generated through learning processing in federated learning environments, then model selection capability is improved, but data privacy and security risks increase
Solution Approach 1:
The system segments the model selection process into two distinct phases: a filtering phase where models are evaluated against user-defined conditions, and a specification phase where selected models are detailed. This segmentation allows the system to handle multiple models while maintaining data privacy by processing conditions locally and only transmitting necessary model metadata, not raw data, to the server.
Solution Approach 2:
The server acts as an intermediary that receives condition information from terminal devices, determines which models satisfy these conditions, and transmits model specifying information back to the terminal device. This intermediary approach enables model selection without requiring the terminal device to directly access or process all training data, thereby reducing data privacy and security risks while maintaining model selection capability.
2Productivity
If condition-based model filtering is implemented, then model selection efficiency is improved, but system complexity increases
Solution Approach 1:
The system performs preliminary filtering of models based on user-defined conditions before providing detailed specifications. By pre-determining which models satisfy the conditions, the system efficiently narrows down the selection process and avoids the complexity of evaluating all models in detail, thereby improving model selection efficiency while managing system complexity.
Solution Approach 2:
Instead of the terminal device evaluating all models locally and then filtering them, the system inverts the approach by having the server perform the filtering operation based on conditions received from the terminal device. This inversion reduces the computational complexity at the terminal device while maintaining efficient model selection through centralized processing.
Data Source
AI summary
An information processing apparatus includes processing circuitry. The processing circuitry receives, from a terminal device, condition information indicating conditions related to a model. The processing circuitry determines whether at least one of multiple models satisfies the conditions. The multiple models are generated by executing learning processing using learning data. The processing circuitry transmits to the terminal device model specifying information that specifies a one of the models satisfying the conditions based on a result of the determination.