Federated Learning for Seizure Detection Models
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Solution Overview
Problem
Current machine learning models for seizure detection and prediction in implanted neurostimulation systems are limited by the need for large datasets, which can be difficult to collect and share due to data privacy and security concerns, leading to models that do not generalize well across patients.
Innovation Solution
A method and system for updating machine learning models in implanted medical devices using federated learning, where updated models are aggregated from multiple devices without directly sharing raw data, allowing for decentralized data storage and improved model generalization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If large datasets are centralized for training machine learning models, then model performance and generalizability improve, but data privacy and security risks increase
Solution Approach 1:
The patent segments the centralized training process into distributed local training sessions across multiple implanted devices. Each device trains its own copy of the machine learning model locally on its private dataset, then shares only model updates (weight changes) rather than raw data. This segmentation allows the system to aggregate knowledge from multiple patients while maintaining data privacy, as the central server receives only encrypted model parameter updates而非原始数据
Solution Approach 2:
The patent introduces an encrypted communication layer as an intermediary between the implanted devices and the central server. Model updates are transmitted through this encrypted channel, which acts as a mediator that protects the underlying data privacy while enabling model aggregation. The encryption scheme ensures that even though model updates are shared, the original patient data cannot be reconstructed or inferred from these updates
2Adaptability or versatility
If raw patient data is shared across multiple locations for model training, then model generalizability improves, but data security and patient consent requirements become more complex
Solution Approach 1:
The patent creates and distributes copies of the machine learning model to multiple implanted devices instead of sharing copies of patient data. Each device maintains its own local copy of the model and trains it independently on local data. The model copies are then updated through aggregation of local training results, eliminating the need for complex data sharing protocols while achieving the same goal of improved model generalizability through multi-patient training
3Object-affected harmful factors
If machine learning models are trained on individual patient data only, then data privacy is maintained, but model performance and generalizability deteriorate
Solution Approach 1:
The patent merges the training capabilities of multiple implanted devices through federated learning, where each device contributes local training results that are aggregated into a global model. This merging occurs at the model parameter level rather than at the data level, allowing the system to combine the benefits of multiple patient datasets while maintaining the privacy protections of individual device data isolation. The aggregation process combines weight updates from multiple devices to create an improved global model that generalizes across patients
Data Source
AI summary
A server for updating a current version of a machine learning model resident in implanted medical devices includes an interface, a memory, and a processor. The interface is configured to receive a plurality of updated versions of the machine learning model from a plurality of remote sources remote from the server. The remote source may be, e.g., implanted medical devices and/or subservers. The processor is coupled to the memory and the interface and is configured to aggregate the plurality of updated versions to derive a server-updated version of the machine learning model, and to transmit the server-updated version of the machine learning model to one or more of the plurality of remote sources as a replacement for the current version of the machine learning model.