Federated Learning Model Update for Small Medical Facilities
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Solution Overview
Problem
Small medical facilities face challenges in generating accurate models due to insufficient training data, which hampers their ability to effectively learn and improve their local models in distributed learning systems.
Innovation Solution
A learning system that includes a central server and multiple sites, where sites with larger data sets, like candidate sites, assist smaller sites by updating their models based on data distributions, selecting the most similar data sets from larger cohorts to enhance model accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If distributed learning is used to protect patient data confidentiality, then data security is improved, but model accuracy deteriorates due to insufficient training data at individual sites
Solution Approach 1:
The patent combines data from multiple sites through federated learning to improve model accuracy while maintaining data security. The server aggregates models from multiple client sites, effectively merging the computational benefits of distributed data without physically combining the sensitive data itself, thus resolving the contradiction between data security and model accuracy.
Solution Approach 2:
The learned model is designed to be universally applicable across different medical sites. By training a generalizable model that can function effectively at multiple sites with varying data volumes, the system achieves both data security (through distributed learning) and acceptable model accuracy (through universal applicability).
2Reliability
If local models are trained at each client site, then data confidentiality is maintained, but model performance deteriorates when training data is insufficient
Solution Approach 1:
The server acts as an intermediary that facilitates model improvement without direct access to client data. It coordinates the federated learning process, aggregates models from multiple clients, and redistributes improved models, enabling performance enhancement while maintaining the confidentiality barrier between client sites and the server.
Solution Approach 2:
Multiple local models from different client sites are merged through aggregation at the server level. This combining of models from sites with different data characteristics compensates for individual data insufficiencies and improves overall model performance while each site maintains its data confidentiality.
3Measurement precision
If more training data is collected at small medical facilities, then model accuracy would improve, but data collection capability worsens due to limited patient volume
Solution Approach 1:
The patent transitions from a single-site data collection approach to a multi-site collaborative approach. Instead of small facilities trying to collect more data locally (one dimension), the system leverages data from multiple sites through federated learning (adding another dimension), thereby achieving sufficient training data volume without requiring individual sites to increase their patient volume.
Solution Approach 2:
The overall training data requirement is segmented across multiple client sites rather than requiring one site to provide all data. Each site contributes a portion of the training process through its local model updates, and the aggregation of these segmented contributions achieves the equivalent of having large-volume training data without concentrating it at a single location.
Data Source
AI summary
A learning system includes processing circuitry. The processing circuitry is configured to acquire a first data distribution for a first data set out of data sets based on a first cohort, to select a second cohort that is used to update a first model out of a plurality of second cohorts on the basis of the acquired first data distribution, and to update the first model on the basis of at least part of a second data set out of data sets based on the selected second cohort.