Distributed Client-Side Recommendation Model Training
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Solution Overview
Problem
Existing big data model training methods face challenges in privacy protection and resource requirements, as they often necessitate uploading sensitive user data to a central server, which is vulnerable to attacks and requires significant storage and computing resources, especially with increasing data quantities and model complexity.
Innovation Solution
A distributed recommendation system construction method where each user's client performs matrix decomposition through collaborative training, locally storing and processing data, thereby avoiding centralization and enabling parallel processing across multiple clients, which improves privacy, security, and training efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If user data is uploaded to a central server for model training, then model training can be performed centrally, but user privacy is compromised and security risks increase
Solution Approach 1:
The patent segments the centralized model training process into distributed training across multiple user terminals. Each terminal independently performs local model training using its own data, eliminating the need to upload sensitive user data to a central server. This segmentation maintains training capability while protecting user privacy by keeping data localized.
2Manufacturing precision
If all user data is stored and processed at a central server, then comprehensive model training is possible, but storage and computing resource requirements increase significantly
Solution Approach 1:
The patent implements local quality by enabling each user terminal to perform model training locally using its own data and resources. This distributed approach eliminates the need for a central server to store and process all user data, significantly reducing the storage and computing resource requirements while maintaining model training accuracy through localized processing.
3Stability of the object's composition
If centralized training is used to ensure data consistency, then model uniformity is maintained, but the system becomes vulnerable to server attacks and data breaches
Solution Approach 1:
The patent segments the centralized data storage and processing architecture into distributed local processing units. Each user terminal maintains its own data locally and performs independent model training, eliminating the single point of failure represented by the central server. This segmentation improves system security and reliability by preventing server attacks from compromising user data while maintaining data consistency through distributed synchronization mechanisms.
Data Source
AI summary
A client device determines a local user gradient value based on a current user preference vector and a local item gradient value based on a current item feature vector. The client device updates a user preference vector by using the local user gradient value and updates an item feature vector by using the local item gradient value. The client device determines a neighboring client device based on a predetermined adjacency relationship. The local item gradient value is sent by the client device to the neighboring client device. The client device receives a neighboring item gradient value sent by the neighboring client device. The client device updates the item feature vector by using the neighboring item gradient value. In response to the client device determining that a predetermined iteration stop condition is satisfied, the client device outputs the user preference vector and the item feature vector.