Two-Sided Dialog Modeling With Hidden-State Federated Training
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Solution Overview
Problem
Existing dialogue systems face challenges in training neural models with private datasets due to privacy concerns and high communication costs, particularly when using federated learning, which can compromise user privacy and overwhelm limited computational resources.
Innovation Solution
A two-sided modeling framework using GPT models, such as GPT-2 and GPT-3, trains neural dialog systems in a federated learning setting by transferring only hidden states and sparsified gradients, avoiding raw data transmission and optimizing communication costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If federated learning is used to train neural models with private datasets, then user privacy is protected, but communication costs increase and computational resources are overwhelmed
Solution Approach 1:
The patent extracts only the essential gradient information needed for model training while discarding redundant data. Specifically, it uses gradient sparsification to select and transmit only the most important gradient components from client devices to the central server, significantly reducing communication overhead while maintaining training effectiveness and privacy protection
Solution Approach 2:
Instead of having the central server send large model parameters to all clients for updating (traditional federated learning), the patent inverts the approach by having clients compute gradients locally and send only these compressed gradient updates to the server for model aggregation. This reversal dramatically reduces communication costs while preserving privacy
2Manufacturing precision
If large pre-trained language models are used for dialogue generation, then performance is improved, but computational resource requirements increase
Solution Approach 1:
The patent segments the dialogue system into two distinct components: a large pre-trained language model for generating high-quality responses, and a smaller adapter model for task-specific adaptations. This segmentation allows the system to maintain high performance while reducing the computational burden on client devices, as the heavy lifting is done by the pre-trained model and only lightweight updates are transmitted
Solution Approach 2:
The patent performs preliminary training on large-scale datasets to pre-train the language model before deployment. This preliminary action allows the model to learn general language patterns and knowledge in advance, so that during federated learning, only task-specific fine-tuning is needed, significantly reducing the computational resources required at client devices
Data Source
AI summary
A central learning model is deployed as a user model and as an assistant model. Sensitive information utterances from a corpus of previously stored conversation language corresponding to user queries and chat agent responses thereto are used to train the user model to become an updated user model and to train the assistant model to become an updated assistant model, respectively. The user model provides user contexts corresponding to user queries to the assistant model and the assistant model provides assistant contexts corresponding to chat agent responses to the user model. During training, the user model does not provide plain-text queries to the assistant model and the assistant model does not provide plain-text responses to the user model. The updated assistant model may facilitate a federated training process produce an updated central model. An updated central model may be used to provide real-time chat agent responses to live user queries.