Privacy-Preserving Distributed Online Learning Model Synchronization
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Solution Overview
Problem
Distributed online learning for personalized predictive models faces challenges in maintaining model consistency across multiple apparatuses while preserving user privacy, as existing synchronization methods expose personal data and lead to divergent models due to different training data.
Innovation Solution
Implementing a method where model updates are received and processed based on synchronization metadata, applying a decay factor to the updates to prevent overcorrection and maintain model accuracy, and updating model parameters using element-wise addition, ensuring privacy by sharing updates rather than raw data points.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Stability of the object's composition
If model synchronization is implemented by copying data to a server, then model consistency across apparatuses is improved, but user privacy is compromised and data handling requirements increase
Solution Approach 1:
The patent extracts only the essential model parameter updates from the training process and transmits them for synchronization, while leaving the raw training data localized on each apparatus. This extraction approach achieves model consistency without exposing personal data to servers or other apparatuses.
Solution Approach 2:
The patent introduces model parameter updates as an intermediary representation that mediates between local training data and global model consistency. These updates serve as a privacy-preserving medium that allows synchronization without direct data sharing.
2Measurement precision
If apparatuses use different training data for local learning, then personalized predictive accuracy is improved, but model divergence across apparatuses increases
Solution Approach 1:
The patent merges model parameter updates from multiple apparatuses through weighted averaging, combining the benefits of diverse local training data while maintaining overall model consistency. The merging process integrates personalized adaptations without allowing complete divergence.
Solution Approach 2:
The patent modifies model parameters through controlled updates and decay factors, allowing apparatuses to adapt to local data characteristics while preventing excessive parameter divergence. The decay factor gradually reduces the impact of local updates over time.
3Speed
If model updates are applied without decay factor, then convergence speed is improved, but model stability and accuracy decrease due to overcorrection
Solution Approach 1:
The patent applies decay factors as a preemptive cushioning mechanism that reduces the magnitude of model parameter updates before they are applied. This prevents overcorrection and maintains model stability while still enabling convergence.
Solution Approach 2:
The patent introduces dynamic decay factors that adaptively adjust the learning process, allowing faster convergence when appropriate while maintaining stability during critical training phases. The decay factor creates a dynamic balance between speed and accuracy.
Data Source
AI summary
Methods, apparatus, systems, and computer program products are provided for providing distributed online learning for personal predictive models that preserves user privacy. An example method comprises receiving model updates based at least in part on synchronization metadata. The model update comprises one or more received model parameter updates. The received model parameter updates correspond to one or more model parameters. The model updates are associated with a particular apparatus. The example method further comprises determining a decay factor based at least in part on the synchronization metadata; applying the decay factor to the one or more received model parameter updates; and updating the one or more model parameters based on the decayed model parameter updates.