Graph Neural Network Training via Federated Multi-Party Computation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing graph neuronal networks face challenges in processing large-scale, complex, and dynamic data from industrial systems with multiple premises, each operated by multiple parties with limited trust in each other. These systems struggle with heterogeneous graphs, intricate relationships, and scalability, while also requiring decentralized data processing to maintain privacy.
Innovation Solution
A computer-implemented method is proposed that trains a graph neuronal network using privacy-preserving multi-party computation and decentralized federated learning. This method preprocesses operational graph data using privacy-preserving techniques and applies end-to-end privacy preservation throughout the training process, ensuring that no party can make inferences about other parties' data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data centralization is used to train graph neuronal networks, then model training efficiency is improved, but data privacy and security are compromised
Solution Approach 1:
The patent segments the centralized training process into distributed federated learning across multiple premises. Each premise trains local graph neuronal networks on its own data, then shares only model updates (not raw data) with a central coordinator. This segmentation enables both distributed privacy preservation and coordinated model training.
Solution Approach 2:
The patent introduces cryptographic intermediaries (secure multi-party computation protocols, homomorphic encryption, or trusted execution environments) that enable collaborative model training without direct data sharing. These intermediaries act as mediators that allow computations on encrypted data, preserving privacy while enabling productivity.
2Reliability
If decentralized federated learning is used to preserve privacy, then data privacy is improved, but model training complexity increases
Solution Approach 1:
Each premise performs self-service by training its own local graph neuronal network on its operational data without requiring centralized data collection. The local training process is autonomous, and only model parameter updates are shared, reducing the complexity burden on any single entity while maintaining privacy.
Solution Approach 2:
The patent employs universal cryptographic primitives (such as homomorphic encryption or secure multi-party computation protocols) that can be applied across different premises and data types. These multi-functional cryptographic tools handle various privacy-preserving operations uniformly, reducing overall system complexity despite the decentralized architecture.
3Measurement precision
If multiple parties process operational graph data, then collaborative inference accuracy is improved, but privacy leakage risk increases
Solution Approach 1:
The patent applies local quality by allowing each party to process and contribute only its local operational graph data to the collaborative model training. Each premise's data characteristics and quality are preserved locally, and the aggregated model benefits from diverse local contributions without any single party's data being exposed to others.
Solution Approach 2:
The patent transforms raw operational data into model parameter updates through the training process. Instead of sharing sensitive raw data, parties exchange transformed parameter representations that capture learning insights without revealing underlying operational details. This parameter transformation maintains inference accuracy while reducing privacy leakage risk.
Data Source
Figure 1
Figure 2~4
Figure 5~7
AI summary
The proposed method comprises: preprocessing respective operational graph data (111-114) related to the technical operations and collected in each of the premises (100, 200) by each of a plurality of parties operating the respective premise (100, 200) using a privacy-preserving multi-party computation technique to obtain preprocessed operational graph data (211-214); and using decentralized federated learning with a privacy-preserving model update method to train the graph neuronal network (3) to provide, in response to the preprocessed operational graph data (211-214) of each of the premises (100, 200) being input, the technical inference (4) as an output. An additional end-to-end privacy preservation technique is applied throughout. The method ensures privacy of operations of each of the parties to a level sufficient for industrial use.