On-Device Model Adaptation for Context-Shifted Security Prediction
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Solution Overview
Problem
Conventional security infrastructure for networked devices relies on resource-intensive, data-heavy training and sporadic updates, which are inefficient and can undermine security in dynamic environments, failing to provide robust, contextualized protection.
Innovation Solution
An on-device adaptation system that uses multi-modal machine learning models to dynamically adjust configurations based on context parameters derived from audio, text, and user interactions, enabling efficient and adaptive security measures by selecting appropriate prediction models and sharing them across devices in a federated learning network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional security infrastructure uses resource-intensive, data-heavy training and sporadic updates, then model coverage and theoretical accuracy are improved, but system efficiency deteriorates and responsiveness to dynamic threats is reduced
Solution Approach 1:
The patent implements dynamic model adaptation by enabling security models to continuously learn and update on-device based on real-time context parameters (audio, text, user interactions) rather than relying on static, periodic updates. This dynamic approach allows the system to maintain high reliability through continuous adaptation while improving efficiency by processing data locally without requiring resource-intensive centralized retraining.
Solution Approach 2:
The system enables self-service through on-device adaptation where each device autonomously trains and updates its own security model using local context data. This eliminates the need for centralized, resource-intensive retraining operations while maintaining up-to-date security protection, thereby resolving the contradiction between reliability and efficiency.
2Measurement precision
If conventional systems perform centralized retraining and updates, then model accuracy is improved, but computational resources and training costs increase significantly
Solution Approach 1:
The patent applies local quality by enabling each device to perform localized model training and adaptation using its own context data. Instead of centralized retraining that consumes massive computational resources, each device maintains and updates its own security model locally, achieving high prediction accuracy for its specific context while minimizing overall computational resource consumption across the network.
Solution Approach 2:
The system segments the centralized training process into distributed on-device adaptation tasks. Each device independently performs model updates based on its local context parameters, dividing the computational workload across multiple devices rather than concentrating it in a centralized system, thereby reducing total energy consumption while maintaining accuracy.
3Stability of the object's composition
If security models are updated sporadically with centralized patches, then system stability is maintained, but adaptability to evolving threats deteriorates
Solution Approach 1:
The patent implements continuous adaptation through on-device learning that operates continuously in the background, monitoring context parameters and updating security models in real-time. This continuous process maintains system stability by gradual adaptation while simultaneously improving threat response adaptability, eliminating the contradiction present in sporadic update models.
Solution Approach 2:
The system incorporates feedback mechanisms where context parameters (audio, text, user interactions) continuously inform model updates. This feedback loop enables the system to maintain stability through controlled, incremental adaptations while rapidly responding to evolving threats, resolving the contradiction between stability and adaptability.
4Measurement precision
If multi-modal context parameters are processed in real-time, then prediction accuracy and threat detection are improved, but system complexity increases
Solution Approach 1:
The patent implements a universal on-device adaptation framework that handles multiple context modalities (audio, text, user interactions) through a unified model structure. This multi-functional approach enables real-time processing of diverse context parameters with high detection accuracy while avoiding the complexity of separate processing systems for each modality, as the single adaptive model handles all contexts uniformly.
Data Source
AI summary
A method and related systems for dynamically adjusting on-device configurations for models based on detected context switches is disclosed. The system can use an on-device language model to determine context parameters in a first mode and then uses the context parameters to determine prediction models or prediction model parameters to use for inputs provided in a second mode.