Federated Learning Model Setup Through Verified Configuration Linking
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Solution Overview
Problem
Existing federated learning models require manual user intervention and high technical expertise for model construction, leading to low efficiency and increased workload.
Innovation Solution
A method and apparatus for constructing federated learning models by associating and verifying pre-created configuration information between participants, enabling automatic model creation through co-training without manual user intervention, ensuring data privacy and security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual user intervention is used for model construction, then model construction can be performed with basic operations, but model construction efficiency is low and workload is high
Solution Approach 1:
The system enables automated model construction where the federated learning management system automatically associates configuration information, verifies parameters, creates model tasks, and coordinates training across multiple participants without requiring manual user intervention at each step, thereby improving efficiency while maintaining ease of use through automation
Solution Approach 2:
Participants pre-create configuration information including model parameters, training settings, and resource allocations before the federated learning process begins. This preliminary preparation allows the system to automatically execute the model construction process without requiring manual intervention during execution, resolving the contradiction between automation and operational simplicity
2Productivity
If automated model construction is implemented, then model construction efficiency improves, but system complexity increases
Solution Approach 1:
The federated learning management system acts as an intermediary that automates the model construction process by associating configuration information from multiple participants, verifying parameters, and coordinating training. This intermediary layer handles the complexity internally while presenting a simplified interface to users, thereby improving efficiency without exposing system complexity to end users
Solution Approach 2:
The automated model construction process is segmented into distinct modular steps: configuration information association, parameter verification, model task creation, and training coordination. Each module handles a specific aspect of the process independently, making the complex automation manageable and maintainable while achieving high construction efficiency
3Reliability
If configuration information is verified automatically, then data security and accuracy are ensured, but processing time increases
Solution Approach 1:
Participants pre-create and prepare their configuration information before the federated learning model construction begins. This preliminary preparation ensures that when the automated verification process runs, the data is already structured and ready for validation, maintaining security and accuracy checks while minimizing the time penalty of verification
Solution Approach 2:
Manual verification processes are replaced with automated computational verification systems that can rapidly check configuration information and parameters. The automated system uses algorithmic validation rules to ensure data security and accuracy without the time-consuming manual review process, resolving the contradiction between thorough verification and processing speed
Data Source
AI summary
A model construction method and an apparatus, and a medium and an electronic device are disclosed. The method is applied to a first participant platform, and includes: associating first configuration information pre-created by a first participant with second configuration information pre-created by a second participant; verifying the first configuration information; sending, to a second participant platform corresponding to the second participant, a second creation request for requesting the creation of the federated learning model, to cause the second participant platform to verify the second configuration information creating a first model task on the basis of a first parameter corresponding to the first configuration information; and performing co-training on the basis of the first model task and a second model task, to obtain the federated learning model.