Distributed Model Bias Correction via Local Training
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Solution Overview
Problem
Traditional machine learning systems face challenges in utilizing data from distributed devices due to data biases and privacy concerns, as well as high bandwidth requirements for data transfer.
Innovation Solution
A system and method that provides a distributed model source to external devices, allowing them to cleanse and correct their local data, train an instance of the model, and generate update reports, which are then used to improve the distributed model while maintaining privacy and minimizing bandwidth usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data is gathered from distributed devices to train machine learning models, then the model can utilize diverse and extensive data, but the data may contain biases that reduce model accuracy
Solution Approach 1:
The system segments the training process into two distinct phases: (1) local model training on distributed devices using local data, and (2) centralized model aggregation where bias correction is applied. This segmentation allows the system to utilize diverse distributed data while applying targeted bias correction at the aggregation stage, resolving the contradiction between data volume and model accuracy.
Solution Approach 2:
The system introduces an intermediary bias correction module that processes model updates from distributed devices before integrating them into the central model. This intermediary component identifies and corrects biases in the distributed data without requiring access to the raw data itself, thereby maintaining model accuracy while utilizing diverse data sources.
2Quantity of substance
If data is transferred from distributed devices to a central location for model training, then the model can be trained with comprehensive data, but the bandwidth required for data transfer becomes extensive
Solution Approach 1:
The system extracts only the essential model updates and gradients from distributed devices rather than transferring the entire dataset. By taking out only the necessary training information (model parameters and gradients) rather than the raw data itself, the system achieves comprehensive model training with minimal bandwidth consumption.
Solution Approach 2:
Instead of transferring original data, the system creates and transfers copies of model updates and training results from distributed devices to the central location. These model copies contain the essential learning information without the bandwidth overhead of transferring the complete distributed datasets, resolving the contradiction between data availability and bandwidth consumption.
3Adaptability or versatility
If data from distributed devices is used for model training, then the model can leverage local data advantages, but privacy concerns arise due to sensitive data on devices
Solution Approach 1:
The system extracts only the trained model parameters and performance metrics from distributed devices, leaving the sensitive raw data localized on each device. This extraction approach enables the central system to benefit from local data utilization while the sensitive data never leaves the device, thereby maintaining privacy security.
Solution Approach 2:
The system introduces an intermediary layer that processes model updates without exposing the underlying sensitive data. The intermediary bias correction module and model aggregation server work with anonymized model parameters rather than raw data, creating a protective barrier that maintains privacy while enabling collaborative model improvement.
4Loss of energy
If data is stored on distributed devices instead of a central location, then bandwidth usage is reduced, but the data may contain regional biases that are not conducive for general-use models
Solution Approach 1:
The system implements a feedback mechanism where the central server provides bias correction guidance and aggregated model information back to distributed devices. This feedback loop enables devices to adjust their local training processes to reduce regional biases, improving model generalizability while maintaining the bandwidth-efficient distributed architecture.
Solution Approach 2:
The central model aggregation server acts as an intermediary that receives model updates from distributed devices with regional biases, applies bias correction algorithms, and produces a generalized model. This intermediary process eliminates regional biases without requiring changes to the bandwidth-efficient distributed data storage architecture.
Data Source
AI summary
Improving a distributed model with distributed data receives an instance of the distributed model from the network-connected computing system, create a cleansed dataset from data stored in the memory with at least biases within the data stored in memory corrected, train the instance of the distributed model with the cleansed dataset, and generate an update report based at least in part by updates to the instance of the distributed model.