Federated Learning Integration Across Intermittent Secure Networks
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Solution Overview
Problem
Building a network that connects multiple organizations with different security approaches is difficult due to security concerns, making it challenging to construct a global model when networks are not constantly connected.
Innovation Solution
Implementing a learning apparatus that establishes secure communication with information terminals in each organization, acquires data sets, and integrates local models using secure communication protocols like VPNs and secure computation technologies to build a global model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If secure communication protocols are implemented to connect multiple organizations with different security approaches, then the ability to construct a global model is improved, but the complexity of the communication system increases
Solution Approach 1:
The patent introduces a server as an intermediary that mediates communication between information terminals of different organizations. The server establishes secure communication channels with each terminal using protocols appropriate for that organization's security requirements, thereby enabling global model construction without requiring direct connections between organizations with different security approaches.
Solution Approach 2:
The communication system is segmented into independent communication channels between the server and each information terminal. Each channel can use different security protocols tailored to the specific organization's requirements, allowing the system to handle multiple security approaches without requiring a unified complex communication framework between all organizations.
2Reliability
If networks of multiple organizations are connected to construct a global model, then the performance of the global model is improved, but the difficulty of building the network increases due to different security approaches
Solution Approach 1:
The server acts as a central intermediary that simplifies network construction by handling all communication with information terminals. This eliminates the need to build direct peer-to-peer networks between organizations with different security approaches, significantly reducing network construction difficulty while still enabling global model performance improvement through data aggregation.
Solution Approach 2:
Instead of having information terminals directly connect to each other to build the network, the approach is inverted by having all terminals connect to a central server. This reverses the network topology from a mesh structure to a star structure, making network construction easier while achieving the same goal of integrating data from multiple organizations.
3Measurement precision
If data is collected from multiple organizations to build local and global models, then the accuracy of the models is improved, but the security risk of data transmission increases
Solution Approach 1:
The server serves as a secure intermediary for data collection from multiple organizations. By routing all data transmission through the server with established secure communication channels, the system achieves high model accuracy through aggregated data while managing security risks through centralized control and protocol enforcement.
Solution Approach 2:
The system changes the security parameter by establishing secure communication protocols specifically for data transmission between the server and information terminals. This allows the system to maintain high model accuracy through data aggregation while transforming the security risk profile through encrypted and authenticated communication channels.
Data Source
AI summary
A learning apparatus includes: a communication establishment unit configured to establish secure communication with an information terminal arranged in a network of each one of organizations; an acquisition unit configured to acquire a data set for each of the organizations from a corresponding one of the information terminals using the secure communication; a learning unit configured to cause a local model to learn the data set; and an integration unit configured to integrate a plurality of local models which have learned a plurality of data sets.


