Federated Recommendation Clustering for Personalized Low-Communication Models
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Solution Overview
Problem
Existing federated recommendation systems fail to address user heterogeneity and require excessive communication between the server and users, leading to inefficiencies in personalized recommendations and high communication burdens.
Innovation Solution
A graph neural network-based Personalized Federated Recommendation (PerFedRec) framework that groups users into clusters, learns user representations, and adapts models to user heterogeneity, while reducing communication burden by selecting only a few representative users for training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If federated recommendation systems process local model parameters for all users to achieve personalized recommendations, then recommendation personalization is improved, but communication burden increases
Solution Approach 1:
The patent segments users into different clusters based on their preferences and behaviors. Instead of processing all users uniformly, the system divides the user base into homogeneous groups and processes only representative users from each cluster. This segmentation reduces the number of users requiring full model parameter processing while maintaining personalized recommendation quality through cluster-specific models.
2Measurement precision
If federated recommendation systems exchange detailed model parameters between server and users, then recommendation accuracy is improved, but communication efficiency deteriorates
Solution Approach 1:
The patent extracts and processes only the essential model parameters needed for personalized recommendations at the user端, rather than exchanging complete detailed model parameters between server and users. The server provides cluster-level model parameters, and users locally combine these with their own interaction data to generate personalized recommendations, reducing communication overhead while maintaining accuracy.
3Manufacturing precision
If federated recommendation systems process complete local model parameters for each user, then personalized recommendation quality is improved, but computational complexity increases
Solution Approach 1:
The patent implements a dynamic two-level processing approach where the system adapts the level of model parameter processing based on user cluster characteristics. For common patterns across users, the system uses pre-processed cluster-level parameters. For individual personalization needs, the system dynamically combines these with user-specific local parameters. This dynamic approach reduces overall computational complexity while maintaining recommendation quality.
Data Source
AI summary
A federated recommendation system with a server and client devices. The server can group client device users into clusters. The server can further: for each respective cluster, process local model parameters associated with local graph neural networks for at least some client device users in the corresponding cluster, to obtain cluster-level model parameters associated with a cluster-level federated model for the corresponding cluster; and process local model parameters associated with local graph neural networks for at least some client device users in each of two or more of the clusters to obtain global model parameters associated with a global federated model. The server can further provide, to a client device, the cluster-level model parameters associated with the corresponding cluster-level federated model and the global model parameters associated with the global federated model, for facilitating generation or update of a personalized recommendation model for the client device user.