Adapting Global Machine Learning Models via Variance-Based Local Training Steps
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Solution Overview
Problem
Existing machine learning models face challenges in balancing global and local customization, as over-customization can lead to loss of beneficial global data insights while under-customization may not optimize local performance, particularly in federated learning scenarios where devices share similar environments and data.
Innovation Solution
A system and method for adapting global models to accommodate personalized local models by using Bayesian hierarchical modeling and variance data to determine the number of local training steps, ensuring device-specific model parameters without straying too far from the global model, thereby achieving a balance between globalization and localization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If local training steps are increased to improve local model performance, then local customization is improved, but the model may over-fit to local data and lose global benefits
Solution Approach 1:
The patent dynamically adjusts the number of local training steps as a controllable parameter. By varying this parameter based on variance data and cohort characteristics, the system optimizes local model performance while preventing over-fitting. The parameter is adjusted to balance between utilizing local data effectively and maintaining generalizability across different cohorts.
Solution Approach 2:
The patent applies partial training actions by limiting the number of local training steps rather than performing exhaustive local training. This partial action approach ensures that local customization is performed to a sufficient degree to improve local performance, but not to the extent of over-fitting and losing global benefits. The training is stopped at an optimal point determined by variance analysis.
2Stability of the object's composition
If local training steps are decreased to retain global model characteristics, then global benefits are preserved, but local performance optimization is insufficient
Solution Approach 1:
The system dynamically adjusts the local training steps parameter based on cohort variance data. When variance is low (indicating similar cohorts), more local training steps are permitted to improve local performance. When variance is high, fewer local training steps are applied to maintain global model characteristics. This parameter adaptation resolves the contradiction between stability and performance.
Solution Approach 2:
The patent introduces dynamic adjustment of training parameters based on runtime conditions and cohort characteristics. The number of local training steps is not fixed but adapts according to variance data and performance requirements, allowing the system to balance between maintaining global characteristics and optimizing local performance based on current conditions.
3Productivity
If more devices are grouped into cohorts for federated learning, then training efficiency is improved, but the heterogeneity of data environments increases
Solution Approach 1:
The patent segments devices into cohorts based on similarity metrics such as data characteristics, device types, or performance profiles. This segmentation allows grouping of devices for efficient federated learning while ensuring that devices within each cohort have relatively homogeneous data environments. The segmentation strategy balances the trade-off between cohort size for efficiency and homogeneity for adaptability.
Solution Approach 2:
The system employs feedback mechanisms by calculating and monitoring variance data within cohorts. This feedback information is used to adjust cohort composition, training parameters, and model updates. The feedback loop ensures that as devices are grouped into cohorts, the system can detect and respond to heterogeneity, adjusting parameters to maintain effective training across diverse environments.
4Reliability
If device-specific model parameters are created through local training, then user privacy is protected, but model accuracy may be compromised due to limited local data
Solution Approach 1:
The patent merges local training results with global model updates in a federated learning framework. Devices perform local training to protect privacy, then their model updates are aggregated with updates from other devices in the cohort. This combining approach allows each device to benefit from both its own local data (maintaining privacy) and the collective knowledge from multiple devices (improving accuracy), resolving the contradiction between privacy protection and model accuracy.
Data Source
AI summary
A system may create a localized machine learning model including one or more customized local parameter values using a global model and variance data. The localized machine learning model may be used by a device or cohort of devices to perform evaluations of data. The localized model may be trained based off a global model that is adjusted and then trained a certain number of steps, where the number of steps is based at least in part on the variance data.