Federated Principal Component Analysis via Local Correlation Integration
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Solution Overview
Problem
The existing method of principal component analysis faces challenges due to the high cost and difficulty of collecting sufficient learning data, leading to potential imbalances in samples and insufficient data analysis quality, as well as increased calculation costs and memory usage when performing analysis on large datasets.
Innovation Solution
An integrated analysis method where client apparatuses perform local computations on learning data to obtain correlations, which are then integrated by a server to derive principal components, reducing data exchange costs and calculation burdens while maintaining data analysis quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If learning data is separately collected by individual users, then data privacy and autonomy are maintained, but sample imbalance occurs and data analysis quality becomes insufficient
Solution Approach 1:
The patent segments the data collection and processing workflow into client-side preprocessing (calculating correlation matrices locally) and server-side integration (combining correlation matrices from multiple clients). This segmentation allows each client to maintain data privacy while contributing to a comprehensive analysis, resolving the contradiction between data autonomy and sample balance.
2Quantity of substance
If pieces of learning data collected separately are gathered in one system, then a sufficient amount of learning data is secured, but communication cost increases hugely
Solution Approach 1:
The patent extracts only the necessary computational results (correlation matrices) from the raw learning data at client devices, rather than transmitting the entire datasets to the server. This extraction approach maintains data sufficiency for analysis while dramatically reducing communication costs and data transfer requirements.
Solution Approach 2:
The patent introduces correlation matrices as an intermediary representation between raw learning data and the final analysis results. These matrices serve as a compressed intermediate form that preserves essential statistical relationships while minimizing data transmission requirements between clients and the server.
3Reliability
If principal component analysis is performed on the gathered large amount of learning data, then comprehensive analysis is achieved, but calculation cost increases and memory becomes insufficient
Solution Approach 1:
The patent performs preliminary computational actions at client devices by calculating correlation matrices from local learning data before transmission. This preprocessing step reduces the dimensionality and complexity of the data, enabling comprehensive analysis at the server without requiring excessive computational resources or memory capacity.
Data Source
AI summary
An integrated analysis method according to one or more embodiments may include: a step of each client apparatus executing computation for obtaining correlation between elements in local samples included in the local learning data; a step of a server apparatus acquiring results of the computation by the client apparatuses; a step of the server apparatus calculating an integration result indicating the correlation between elements of all of the local samples of all of the local learning data, by integrating the results of computation acquired from the client apparatuses; a step of the server apparatus deriving one or more principal components from the calculated integration result by performing principal component analysis; and a step of the server apparatus outputting information regarding the one or more derived principal components.