Federated Latent Feature Sharing for Precise Recommendation Models
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Solution Overview
Problem
Conventional training techniques for predictive models in recommendation systems constrain precision, resilience, and adaptability due to limitations in a single model's configuration or parameters, leading to inaccurate and context-insensitive suggestions.
Innovation Solution
A multi-model system that trains machine learning models separately on different user devices, using one model as a ground truth to enhance another, integrating them to generate personalized recommendations, thereby improving accuracy and capturing complex relationships between features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional training techniques are used for predictive models, then the model configuration and parameters are limited, but the precision, resilience, and adaptability of recommendations are constrained
Solution Approach 1:
The patent segments the training process by training multiple machine learning models separately on different user devices using federated learning. Each device trains its own model with local data, creating specialized models that can be later integrated. This segmentation allows each model to adapt to local conditions while maintaining overall system precision through integration.
Solution Approach 2:
The patent combines multiple separately trained machine learning models into an integrated system. Models from different user devices are merged, with one model serving as ground truth to enhance another. This merging approach preserves the adaptability benefits of separate training while achieving high recommendation precision through model integration.
2Device complexity
If a single machine learning model is used, then the model structure is simple, but the system cannot capture complex relationships between features
Solution Approach 1:
Instead of using a single complex model, the patent segments the modeling function across multiple simpler models trained on different devices. Each model captures local feature relationships, and the integration of these models collectively captures complex relationships that would be difficult for a single model to achieve.
Solution Approach 2:
The patent uses one machine learning model as an intermediary (ground truth) to enhance another model. This intermediary model helps transfer knowledge and improve the target model's ability to capture complex feature relationships without requiring the target model to be overly complex itself.
3Productivity
If conventional training methods are used, then training is faster, but overfitting occurs and noise filtering is insufficient
Solution Approach 1:
The patent segments the training process across multiple independent devices using federated learning. Each device trains locally with its own data, which naturally provides regularization and reduces overfitting. The distributed nature of training maintains productivity while improving reliability through diverse local training experiences.
Solution Approach 2:
The patent implements feedback mechanisms where models are trained on local data, evaluated, and then used to enhance other models. The ground truth model provides feedback to enhance the target model, creating a feedback loop that improves noise filtering and prevents overfitting while maintaining training efficiency.
Data Source
AI summary
A method and related system may send a model to first client devices and second client devices, where the model uses an initial set of features, and where the first client devices generates a first latent feature type based on first initial feature of the first client devices, and where the second client devices generates a second latent feature type based on second initial feature of the second client devices. The method may include obtaining first latent feature for the first latent feature type from the first client devices and second latent feature for the second latent feature type from the second client devices. The method may include generating a refined model based on the model, wherein the refined model uses, as inputs, features of the first latent feature type and the second latent feature type based on the first latent feature and the second latent feature values.