Federated Transfer Learning for Heterogeneous Data Alignment
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Machine learning models in healthcare and other fields face performance issues due to insufficient data, as data from different sources is often siloed and cannot be combined due to privacy regulations, limiting the ability to train powerful models that provide insights and predictions.
Innovation Solution
Federated transfer learning is employed, allowing multiple computing devices to share model weights without sharing data, aligning input spaces through techniques like maximum mean distance and covariance loss, centroid matching, conditional tabular generative adversarial networks, optimal transport, and prototype class networks to enable training across non-overlapping feature spaces.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data from different sources is combined to train machine learning models, then model performance and insights are improved, but privacy regulations and data silos prevent data sharing
Solution Approach 1:
The patent introduces federated transfer learning as an intermediary mechanism that enables model training across multiple data sources without direct data sharing. Local models are trained on distributed data silos, and only model weights/parameters are shared and aggregated centrally, bypassing the data sharing restriction while achieving collaborative model performance improvement
Solution Approach 2:
The patent creates copies of model parameters and weights that can be shared across the network. Instead of sharing original data, the system replicates and distributes trained model components to multiple endpoints, enabling collaborative learning while maintaining data privacy and overcoming data silo restrictions
2Adaptability or versatility
If federated transfer learning is used to share model weights without data sharing, then privacy is protected and data silos are maintained, but input spaces must be aligned through complex techniques
Solution Approach 1:
The patent transforms the feature spaces of different data sources into a common parameter space through dimensionality reduction and feature mapping techniques. By changing the representation parameters of heterogeneous input data into unified embeddings, the system enables model weight sharing while reducing the complexity of input space alignment
Solution Approach 2:
The patent creates a universal feature representation that can handle multiple types of input data from different sources. The aligned input space serves as a multi-functional interface that accommodates various data formats and feature types, simplifying the federated learning process across diverse data silos
3Adaptability or versatility
If heterogeneous data sources are used for training, then model versatility is improved, but classification accuracy suffers due to non-overlapping feature spaces
Solution Approach 1:
The patent merges heterogeneous feature spaces from multiple data sources into a unified feature representation. By combining the strengths of different data sources and aligning their feature spaces, the system achieves both model versatility from heterogeneous data and classification accuracy through unified feature processing
Data Source
AI summary
The technology disclosed relates to a system and method of exporting learned features between federated endpoints whose learning is confined to respective training datasets. The system includes logic to access a first training dataset to train a first federated endpoint and a second training dataset to train a second federated endpoint. The first and second training datasets have first and second sample sets that share one or more shared sample features. The shared sample features are common between the first and second sample sets. The system includes logic to train a first generator on the first federated endpoint. The system includes logic to use the first trained generator for a second inference on a second performance task executed on the second federated endpoint.