Federated Learning System for Medical Data Privacy
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Solution Overview
Problem
Traditional machine learning methods require central data collection and storage, raising concerns about data privacy, ownership, and security, as well as human-aided labor for data preparation and model training, which can be inefficient and invasive.
Innovation Solution
Federated learning system utilizing edge devices to collect and process data locally, sending only derived insights (updated model parameters) to a cloud-based aggregator, enabling decentralized model training and reducing privacy concerns by keeping raw data on devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional machine learning methods are used with central data collection, then model training can be performed with aggregated data, but data privacy and security concerns increase
Solution Approach 1:
The system segments data processing by implementing federated learning where each edge device trains local models independently on its own data, avoiding central aggregation of raw data. Only model parameters are shared, not the underlying data, thus maintaining privacy while enabling collaborative learning.
Solution Approach 2:
The patent introduces an intermediary aggregation server that coordinates between edge devices without accessing raw data. The server aggregates model parameters from multiple devices to create improved global models, acting as a mediator that enables collaboration while preserving data privacy.
2Measurement precision
If human-aided labor is used for data preparation and labeling, then data quality can be improved, but labor efficiency decreases
Solution Approach 1:
The system implements self-service through automated data labeling using machine learning models that train on unlabeled data without human intervention. The models automatically identify patterns, classify data, and generate labels, eliminating the need for manual annotation while maintaining high data quality through iterative training processes.
Solution Approach 2:
The patent replaces mechanical human labor with automated machine learning systems that perform data preparation, cleaning, and labeling tasks. The system uses algorithms to automatically process data, transforming manual operations into automated computational processes that scale efficiently.
3Adaptability or versatility
If raw data is sent to central servers for processing, then comprehensive analysis can be performed, but security risks increase
Solution Approach 1:
The system extracts only the necessary information (model parameters) from the raw data for sharing and analysis, while leaving the sensitive raw data on local devices. This extraction approach enables comprehensive analysis through parameter aggregation without exposing centralized systems to data breach risks.
Solution Approach 2:
The patent segments data processing into distributed local computations and centralized parameter aggregation. Each edge device maintains its own data locally while contributing only processed model parameters to the global system, creating security isolation between data storage and analysis functions.
Data Source
AI summary
Method and system with federated learning model for health care applications are disclosed. The system for federated learning comprises multiple edge devices of end users, one or more federated learner update repository, and one or more cloud. Each edge device comprises a federated learner model, configured to send tensors to federated learner update repository. Cloud comprises a federated learner model, configured to send tensors to federated learner update repository. Federated learner update repository comprises a back-end configuration, configured to send model updates to edge devices and cloud.