Distributed Neural Network Training via Gradient Exchange
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Solution Overview
Problem
Current neural network models face challenges in training due to the need for large datasets, storage requirements, accessibility issues, and scalability problems, particularly in speech processing where data is large and confidential, making it difficult to improve automatic speech recognition systems effectively.
Innovation Solution
A distributed system is introduced where a master device and slave devices collaborate to train neural network models, using a teacher-student configuration to exchange weight or gradient information, allowing asynchronous data updates without sharing private data, thus overcoming data accessibility and storage constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a large comprehensive dataset is gathered to improve prediction accuracy, then the accuracy and reliability of speech processing systems is improved, but the storage capacity requirements and data accessibility issues worsen
Solution Approach 1:
The training process is segmented across multiple devices (master device and slave devices), where each device maintains its own data locally. The neural network model is divided into multiple versions distributed across devices, allowing training without centralizing large datasets. This segmentation enables accurate predictions while avoiding the need for centralized large-scale data storage.
Solution Approach 2:
Configuration data acts as an intermediary between slave devices (with private data) and the master device. Instead of directly sharing private data or large datasets, devices exchange compressed configuration data (weights, gradients, hyperparameters) that serves as a mediator to transfer training information without requiring large storage capacities or direct data access.
2Productivity
If training datasets are moved between storage devices for processing, then the training capability is improved, but the time required for data transfer increases
Solution Approach 1:
The invention extracts only the essential training information (configuration data including weights, gradients, and hyperparameters) from the large training datasets. This extracted configuration data is what is actually needed for model training, allowing devices to train efficiently without moving or processing entire large datasets, thereby eliminating time-consuming data transfer operations.
Solution Approach 2:
The system transforms large datasets into compressed parameter representations (configuration data with weights, gradients, hyperparameters). This parameter transformation reduces the data volume from gigabytes/terabytes to manageable sizes that can be quickly exchanged between devices, dramatically reducing transfer time while maintaining training effectiveness.
3Measurement precision
If private data is shared across devices for collaborative training, then the model performance is improved, but data privacy and security are compromised
Solution Approach 1:
Configuration data serves as a privacy-protecting intermediary that enables collaborative training without exposing private data. Slave devices process their private data locally and only share aggregated configuration data (weights, gradients) with the master device. This intermediary mechanism allows model performance improvement through collaboration while maintaining data privacy, as the actual private data never leaves the slave devices.
Solution Approach 2:
Each device maintains its own copy of the neural network model (multiple versions) and trains locally using its private data. Instead of sharing private data, devices share copies of their model parameters and configuration data. This copying approach enables collaborative learning while keeping original private data confined to each device, preventing privacy breaches.
4Adaptability or versatility
If multiple versions of the neural network model are maintained across devices, then the flexibility and scalability are improved, but the device complexity increases
Solution Approach 1:
The system segments the neural network model into multiple versions distributed across different devices, with each device managing a specific version. This segmentation provides flexibility as each device can independently train and update its version using local data and resources. The master device coordinates by exchanging configuration data with slave devices, enabling scalable multi-device training without requiring complex centralized management of all model versions simultaneously.
Data Source
AI summary
Training and enhancement of neural network models, such as from private data, are described. A slave device receives a version of a neural network model from a master. The slave accesses a local and/or private data source and uses the data to perform optimization of the neural network model. This can be done such as by computing gradients or performing knowledge distillation to locally train an enhanced second version of the model. The slave sends the gradients or enhanced neural network model to a master. The master may use the gradient or second version of the model to improve a master model.