Federated Decision-Making Model Coordination for Privacy
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Solution Overview
Problem
Existing methods for federated learning and decision-making across data terminals face challenges in coordinating decision-making while ensuring data privacy, leading to difficulties in accurate decision-making due to data privacy concerns and the need for global information.
Innovation Solution
A privacy-enhanced federated decision-making method that trains a global decision-making model using local information from each data terminal, allowing for adaptive use of a federated decision-making model coordinated by a federated coordinator, enabling accurate and flexible decision-making across multiple data terminals without exposing raw data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is encrypted before transmission to protect privacy, then data security during transmission is improved, but the data cannot be legally used by collectors and coordination between terminals deteriorates
Solution Approach 1:
The patent extracts only the necessary model parameters and decision-making information from the raw data, transmitting these extracted elements between terminals while keeping the original private data localized. This allows coordination without exposing or transmitting the sensitive raw data itself, resolving the contradiction between security and usability.
Solution Approach 2:
The patent introduces a federated coordinator as an intermediary that manages the decision-making coordination between terminals. The coordinator receives anonymized feedback from terminals, aggregates it to form global decisions, and distributes them back, enabling coordination without direct data exchange between terminals.
2Reliability
If raw data is kept localized to protect privacy, then data security is improved, but decision-making accuracy deteriorates due to lack of global information
Solution Approach 1:
The patent implements a feedback mechanism where terminals send anonymized feedback information (such as loss values, gradient information, or decision outcomes) to the federated coordinator. The coordinator aggregates this feedback and uses it to update the global decision-making model, which is then distributed back to terminals. This feedback loop enables accurate global decision-making without requiring access to raw private data.
Solution Approach 2:
The patent creates and distributes copies of the global decision-making model to each terminal. Each terminal uses its local copy to make decisions based on local data, and the model copies are continuously updated through the federated learning process. This allows terminals to benefit from global information while keeping their raw data localized.
3Reliability
If federated learning is used to keep data localized, then data privacy is improved, but communication overhead increases due to multiple rounds of model exchange
Solution Approach 1:
The patent extracts and transmits only the essential model parameters and aggregated feedback information between the coordinator and terminals, rather than exchanging complete models or raw data. This selective extraction of necessary information reduces the volume of communication while maintaining the privacy-preserving federated learning framework.
4Reliability
If encryption is applied to protect data during transmission, then security against eavesdropping is improved, but the collector cannot legally use the collected data for coordination
Solution Approach 1:
The patent extracts only the necessary model parameters and decision-making information from the raw data, transmitting these extracted elements between terminals while keeping the original private data localized. This allows coordination without exposing or transmitting the sensitive raw data itself, resolving the contradiction between security and usability.
Data Source
AI summary
A privacy-enhanced federated decision-making method, apparatus, system and a storage medium are provided for training a global decision-making model for ensuring data privacy of data terminals. Each federated data terminal reports information about a local decision-making model to a federated coordinator, and a federated coordinator trains a global decision-making model by using the information about the local decision-making model reported by the federated data terminals. The trained global decision-making model can be used for coordinating decision making of the federated data terminals, such as coordinating a decision-making sequence of the federated data terminals or coordinating whether the federated data terminals need to participate in a decision-making task. The method resolves the problem of difficult coordination across the data terminals, and improves the decision-making accuracy of the data terminals. The federated data terminals adaptively use the federated decision-making model for improving the decision-making flexibility of the federated data terminals.