Mobile NLP Model Segmentation for Privacy and Latency
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Solution Overview
Problem
Conventional server-side natural language understanding models are not well-suited for end-to-end encrypted environments, as they require significant processing, memory, and power resources, leading to reduced performance and accuracy when moved to end-user devices with limited resources, resulting in increased latency and battery usage.
Innovation Solution
The solution involves optimizing model architecture and training for mobile devices by representing word embeddings at the byte level, using mobile-friendly operators, training smaller models with larger models as teachers, and implementing a sub-model funnel to reduce resource usage and latency, while ensuring data privacy through secure storage and user consent.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If natural language processing models are moved from central servers to end-user devices, then data privacy is improved through end-to-end encryption, but processing resources and memory capacity deteriorate
Solution Approach 1:
The patent segments the natural language processing model into smaller components that can be efficiently executed on mobile devices. The model architecture is divided into multiple layers and functions that can be distributed across available device resources, allowing privacy-preserving processing without requiring a single large model that would exceed device capacity.
Solution Approach 2:
The patent applies parameter changes by optimizing model hyperparameters, reducing model size, and adjusting computational precision to match mobile device capabilities. This includes using quantized weight representations and pruning unnecessary model components to fit within limited memory and processing constraints while maintaining adequate accuracy.
2Quantity of substance
If model size is reduced to fit mobile devices, then memory usage is improved, but processing accuracy deteriorates
Solution Approach 1:
The patent applies local quality by assigning different levels of model complexity to different processing contexts. Frequently accessed model components are optimized for speed and small memory footprint, while less frequently used but more accurate components are available when needed. This creates a hierarchical structure where simpler models handle common cases and more complex models handle edge cases.
Solution Approach 2:
The patent introduces intermediary processing layers that bridge the gap between compressed model representations and full-precision outputs. These intermediary layers perform gradient approximation and error correction to maintain accuracy despite the reduced model size, acting as mediators between the constrained model and the required processing precision.
3Manufacturing precision
If complex models are used for accurate natural language processing, then processing accuracy is improved, but latency and battery consumption worsen
Solution Approach 1:
The patent applies preliminary action by pre-computing and caching frequently accessed model components and intermediate results on the mobile device. This includes pre-loading vocabulary embeddings, pre-computing attention masks, and caching recent processing results to avoid redundant computations, thereby reducing latency without sacrificing accuracy.
Solution Approach 2:
The patent implements dynamic model execution that adapts processing depth and complexity based on input characteristics and device state. For simple queries, the model performs shallow processing with fewer computational steps, while complex queries trigger deeper processing. The system dynamically adjusts batch sizes, activation functions, and processing parallelism to optimize the trade-off between accuracy and latency in real-time.
Data Source
AI summary
Exemplary embodiments relate to methods, mediums, and systems for moving language models from a server to the client device. Such embodiments may be deployed in an environment where the server is not able to provide modeling services to the clients, such as an end-to-end encrypted (E2EE) environment. Several different techniques are described to address issues of size and complexity reduction, model architecture optimization, model training, battery power reduction, and latency reduction.