Federated Learning Encoder for Heterogeneous Data Interoperability
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Solution Overview
Problem
Distributed machine learning in healthcare faces challenges due to fragmented and privacy-concerned electronic health records, with heterogenous data distributions and schemas across institutions, leading to interoperability issues and performance degradation in federated learning.
Innovation Solution
A learning system deploys an interoperable distributed learning environment that trains a neural network encoder to map heterogenous datasets into a common latent feature space, allowing institutions to collaborate without exposing raw data, using a federated learning framework with autoencoders and task neural networks, eliminating the need for separate extract, transform, and load modules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If federated learning is used to train models across multiple institutions, then data privacy is protected, but interoperability issues arise due to heterogenous data distributions and schemas
Solution Approach 1:
The patent introduces a central coordination server as an intermediary that manages the federated learning process. The server coordinates between multiple client institutions, handling the heterogeneity of data schemas and distributions by providing a unified interface for model training. This intermediary layer enables interoperability without requiring direct data sharing between institutions, thus preserving privacy while facilitating collaborative learning.
Solution Approach 2:
The patent employs parameter changes by dynamically adjusting model architecture and training parameters to accommodate different data schemas across institutions. The system can adapt the encoder and task neural network parameters to handle varying data formats, transforming heterogenous data into a common latent space representation that works across all participating institutions.
2Productivity
If raw data is shared across institutions for machine learning, then model performance improves, but privacy security deteriorates
Solution Approach 1:
The patent segments the machine learning model into two parts: a client-side encoder that processes local data and a central task neural network that performs the actual prediction. Only the encoder parameters and transformed features are shared, never the raw data. This segmentation allows model performance to improve through distributed computation while maintaining privacy security by keeping sensitive information local to each institution.
Solution Approach 2:
The patent extracts only the necessary information from raw data through the encoder, transforming it into a compressed latent space representation. This extraction process removes sensitive details while preserving the essential patterns needed for model training. The task neural network then operates on these extracted features, achieving good model performance without ever accessing the original raw data.
3Ease of manufacture
If separate ETL modules are used for data processing, then data transformation is achieved, but system complexity increases
Solution Approach 1:
The patent merges the extraction, transformation, and loading functions directly into the neural network encoder. Instead of separate ETL modules, the encoder performs all necessary data processing operations as an integrated component of the machine learning model. This consolidation reduces system complexity by eliminating multiple separate processing stages while maintaining full data transformation capability.
Solution Approach 2:
The encoder is designed as a universal component that handles multiple functions: data extraction, schema transformation, and feature representation. This multi-functional design eliminates the need for separate ETL modules, reducing overall system complexity while providing comprehensive data processing capabilities across different institutions with varying data formats.
Data Source
AI summary
A learning system deploys, to one or more client devices, modules to be deployed in a learning environment of a respective client node. The learning environment of a respective client node may include modules for the client device (or client node) to collaborate with the central learning system and other client nodes via a distributed learning (e.g., federated learning, split learning) framework. In one embodiment, the learning system deploys an interoperable distributed learning environment for training a neural network encoder which can be used in heterogenous datasets to transform the heterogenous datasets across different institutions or entities into a common latent feature space. After training, the learning system receives data instances including a set of features and labels from different client nodes and trains a task neural network model configured to receive features in the latent space and generate an estimated label from the received data instances.