Federated Learning for Distributed Medical Networks
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Solution Overview
Problem
Hospitals face inefficiencies in sharing pertinent patient medical data, particularly for improving neural networks, due to geographical barriers and existing data sharing challenges.
Innovation Solution
A distributed medical network system that trains machine learning models hosted by local hospitals, involving data input, loss generation, parameter gradient calculation, global update generation, and model updating across multiple local computers, facilitating efficient data sharing and model enhancement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If hospitals share patient medical data across geographical locations, then model training effectiveness is improved, but data privacy and security risks increase
Solution Approach 1:
A central server acts as an intermediary that coordinates federated learning across multiple hospitals. The server aggregates local parameter gradients from each hospital's machine learning models without directly accessing or storing the actual patient medical data, enabling collaborative model training while maintaining data privacy and security at each local institution.
2Object-affected harmful factors
If hospitals train machine learning models locally with their own data, then data privacy is maintained, but model performance and generalization capability deteriorate
Solution Approach 1:
The training process is segmented into local and global components. Each hospital trains its local machine learning model independently on its own patient data, maintaining data privacy. The local parameter gradients from each segmented training process are then aggregated at a central server to achieve global model improvement, combining the benefits of local data control with collaborative learning.
3Productivity
If patient medical data is centralized for training, then model training efficiency is improved, but data transmission time and network bandwidth requirements increase
Solution Approach 1:
The patent extracts only the essential information needed for model training—the parameter gradients—from each hospital's local data, rather than transmitting the actual patient medical data. This extraction approach maintains model training efficiency by providing sufficient training signals while dramatically reducing data transmission time and network bandwidth requirements.
4Productivity
If federated learning is implemented across multiple hospitals, then data sharing efficiency is improved, but system complexity and coordination overhead increase
Solution Approach 1:
The central server provides universal coordination functionality for the federated learning system, handling multiple tasks including aggregation of parameter gradients, management of model versions, and coordination of training schedules across all participating hospitals. This multi-functional approach simplifies the overall system architecture by consolidating coordination complexity in a single universal component.
Data Source
AI summary
Methods and systems for enhancing a distributed medical network. For example, a computer-implemented method includes inputting training data corresponding to each local computer into their corresponding machine learning model; generating a plurality of local losses including generating a local loss for each machine learning model based at least in part on the corresponding training data; generating a plurality of local parameter gradients including generating a local parameter gradient for each machine learning model based at least in part on the corresponding local loss; generating a global parameter update based at least in part on the plurality of local parameter gradients; and updating each machine learning model hosted at each local computer of the plurality of local computers by at least updating their corresponding active parameter set based at least in part on the global parameter update.