Federated Learning Cluster Feedback for Broad Model Applicability
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Solution Overview
Problem
Machine learning models face challenges in securely training on user data while ensuring applicability across diverse user groups, as conventional techniques often result in models that are not uniformly effective for all users due to metadata biases.
Innovation Solution
Implementing cluster-based feedback in federated learning by identifying user clusters with higher applicability, applying positive bias to model states from one cluster and negative bias to another, and iteratively refining the model until a threshold score is met.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional federated learning is used to train machine learning models on user data, then user data security is maintained, but the model's applicability across diverse user groups deteriorates due to metadata biases
Solution Approach 1:
The patent segments users into different clusters based on metadata features. By dividing the user population into distinct groups (clusters) and training separate model instances for each cluster, the system maintains data security through federated learning while improving model applicability by creating specialized models tailored to each user group's characteristics
Solution Approach 2:
The patent applies local quality by creating cluster-specific model instances with customized parameters for each user cluster. Instead of using a single universal model, the system trains and deploys localized model versions that are optimized for each cluster's specific characteristics, thereby improving overall model applicability while maintaining security through distributed training
2Productivity
If machine learning models are trained to favor users with certain metadata features, then training efficiency is improved, but fairness and universal applicability deteriorate
Solution Approach 1:
The patent segments the user base into multiple clusters based on metadata features, allowing efficient training within each cluster while ensuring fair representation across all groups. By training separate model instances for each cluster, the system maintains training efficiency through focused learning while improving fairness by giving each user group dedicated model attention
Solution Approach 2:
The patent changes model parameters specifically for each cluster based on their metadata characteristics. By adjusting model parameters to suit each cluster's needs rather than using fixed parameters for all users, the system achieves efficient training for each group while ensuring fair and universal applicability across diverse user populations
Data Source
AI summary
A method for generating a cluster-based machine learning model based on federated learning with cluster feedback includes providing a current machine learning model to a plurality of user devices that train the current machine learning model, receiving respective model states, generating updated model states, causing the plurality of user devices to obtain a respective instance of an updated machine learning model based on the updated model states, receiving an applicability feedback for the updated machine learning model for each of the plurality of user devices, determining a plurality of user clusters including a subset of the plurality of user devices, identifying a first user cluster and a second user cluster, the first user cluster having a higher cluster applicability feedback than the second user cluster, receiving the additional model states from the clusters and updating the updated machine learning model to generate the cluster-based machine learning model.