Federated Learning for Privacy-Preserving Cargo Data Collaboration
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Solution Overview
Problem
Sharing knowledge and data between separate entities managing cargo, such as customs organizations, is challenging due to privacy concerns and regulatory restrictions, making it difficult to collaborate effectively in cargo data processing and risk assessment.
Innovation Solution
A method that enables entities to build a common way of processing cargo data locally without sharing actual cargo data or knowledge, using local learners to create and share models encoded as neural weights or gradients, allowing for enhanced collaboration while adhering to privacy regulations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If entities share cargo data and knowledge directly, then collaboration effectiveness and processing capability are improved, but privacy security and regulatory compliance deteriorate
Solution Approach 1:
The patent extracts the essential learning patterns and knowledge from cargo data by training local machine learning models, then shares only the model parameters (weights and gradients) rather than the original cargo data itself. This separation allows collaboration while preserving privacy, as the sensitive data remains local but the learned knowledge is shared.
Solution Approach 2:
The patent introduces federated learning as an intermediary framework that enables indirect knowledge sharing. Instead of direct data exchange, entities collaborate through a centralized server that coordinates the aggregation of local model updates, acting as a mediator that facilitates collaboration without exposing sensitive cargo data.
2Adaptability or versatility
If entities share cargo data and knowledge directly, then common processing standards and risk assessment capabilities are improved, but regulatory compliance and data protection deteriorate
Solution Approach 1:
The patent extracts transferable knowledge from local cargo processing operations into machine learning model parameters. These parameters capture common processing patterns and risk assessment logic that can be applied across different entities without requiring sharing of the actual cargo data that would trigger regulatory concerns.
Solution Approach 2:
The patent creates a universal federated learning system that can be applied across multiple customs entities and cargo processing organizations. The shared model parameters provide universal risk assessment capabilities that work across different jurisdictions and data formats while maintaining compliance with local regulations through the federated architecture.
3Measurement precision
If entities collaborate extensively, then cargo data processing capability and risk assessment are improved, but data transmission security and privacy protection deteriorate
Solution Approach 1:
The patent extracts only the essential numerical parameters (weights and gradients) that represent learned knowledge from cargo data processing. These extracted parameters are shared across entities through the federated learning system, enabling improved risk assessment accuracy without transmitting the actual cargo data that would constitute information loss or exposure.
Data Source
AI summary
A computer-implemented method is provided for sharing, between a plurality of entities managing cargo, experience of cargo data processing by the plurality of entities. The method includes a local learner of each entity building a local model of the cargo data processing by the entity in the plurality of entities, a global learner, separate from the plurality of entities, obtaining at least a first relevant part of the respective local models built by each of the respective local learners, the global learner building a global model of the cargo data processing by the plurality of entities, the local learner of each entity obtaining at least a second relevant part of the global model built by the global learner, and the local learner of each entity outputting data about the cargo data processing by the entity in the plurality of entities.