Federated Learning Platform Using Statistical Feature Extraction
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Solution Overview
Problem
Existing federated learning systems face challenges in real-time operations, such as complex event processing, due to excessive computational overhead, heterogeneous data sources, and varying data freshness and granularity, which complicates the selection of hyperparameters and model architecture.
Innovation Solution
A federated machine-learning platform that dynamically assesses the relevance of data using statistical tests and behavior learning relevance analysis, allowing for the construction of robust models across heterogeneous data sources without compromising user privacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If federated learning is used to improve statistical models by allowing knowledge sharing without compromising user privacy, then model flexibility and power are improved, but excessive computational overhead is incurred
Solution Approach 1:
The patent extracts and processes only the most relevant features from distributed data sources using local statistical tests, rather than transmitting or processing all raw data. This selective extraction of meaningful information reduces computational overhead while maintaining model accuracy by focusing resources on the most impactful features.
Solution Approach 2:
The system performs preliminary statistical analysis and feature engineering at each distributed node before model training. By pre-processing data locally to identify and extract relevant features using statistical tests, the system reduces the computational burden during the actual federated learning process, as the model only needs to process already-refined features rather than raw data.
2Reliability
If data is distributed across isolated silos within companies or across companies, then data privacy is protected, but it becomes difficult to break barriers between different data sources
Solution Approach 1:
The patent implements a universal feature engineering framework that can operate across heterogeneous data sources with different formats, structures, and privacy constraints. The system uses standardized statistical tests and feature extraction methods that can be applied universally to various data types, enabling seamless integration across organizational boundaries while maintaining each entity's data sovereignty.
Solution Approach 2:
The system introduces an intermediary layer of engineered features that acts as a mediator between distributed data sources and the federated learning model. Instead of directly accessing or sharing raw data, nodes communicate through this intermediate feature representation, which encapsulates the essential information while preserving data privacy and enabling cross-organizational collaboration.
3Reliability
If heterogeneous data sources with varying data freshness and granularity are used, then model robustness is improved, but hyperparameter selection and model architecture become complicated
Solution Approach 1:
The system automatically adapts statistical parameters and feature engineering configurations based on the characteristics of each data source. By dynamically adjusting parameters such as aggregation windows, statistical test thresholds, and feature selection criteria according to data freshness and granularity, the system handles heterogeneity without requiring manual configuration of complex hyperparameters for each data source.
Solution Approach 2:
The feature engineering pipeline performs self-adjustment by automatically detecting data characteristics and configuring appropriate statistical tests and transformations. Each node autonomously determines the optimal feature extraction parameters for its local data, eliminating the need for centralized hyperparameter tuning and reducing overall system complexity while maintaining robustness across heterogeneous sources.
Data Source
AI summary
Provided is a process including: receiving a data token to be passed from a first node to a second node; retrieving machine learning model attributes from a collection of one or more of the sub-models of a federated machine-learning model; determining based on the machine learning model attributes, that the data token is learning relevant to members of the collection of one or more of the sub-models and, in response, adding the data toke to a training set to be used by at least some members of the collection of one or more of the sub-models; determining a collection of data tokens to transmit from the second node to a third node of the set of nodes participating in a federated machine-learning model; and transmitting the collection of data tokens.