Topological Data Analysis for Anomalous Data Detection in Federated Learning
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Solution Overview
Problem
Current federated learning systems lack effective methods to detect bad quality or anomalous training data, which can compromise the shared model by introducing undesirable parameters.
Innovation Solution
The use of historical data profiles and topological data analysis to identify problematic data before local model training, ensuring data integrity and protecting privacy by filtering data at the client device level.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conventional outlier detection techniques (fixed cutoffs) are used, then the system is simple to implement, but accuracy and adaptability to local circumstances deteriorate
Solution Approach 1:
The patent changes the detection parameters from fixed cutoffs to dynamic thresholds based on historical data profiles and topological invariants. The system computes statistical parameters (mean, variance, skewness, kurtosis) from historical data and uses these to establish adaptive detection thresholds that adjust to local data characteristics, thereby improving accuracy while maintaining reasonable system complexity.
Solution Approach 2:
The system implements feedback by continuously monitoring data updates and comparing them against historical data profiles. The topological data analysis provides feedback about the structural characteristics of data distributions, enabling the system to adapt its detection criteria based on observed patterns in the federated learning environment.
2Extent of automation
If data is sent to central node for review, then centralized control is achieved, but privacy protection and data transmission overhead worsen
Solution Approach 1:
The patent applies preliminary action by performing data quality assessment and anomaly detection at the client device before data is transmitted to the central node. The system uses historical data profiles stored locally and topological data analysis to evaluate data updates in advance, filtering out anomalous data locally and only transmitting cleaned data to the central node, thus preserving privacy and reducing transmission overhead.
Solution Approach 2:
The system introduces an intermediary layer of local data profiling and topological analysis that mediates between data generation at client devices and centralized aggregation. This intermediary process enables automated data quality control without requiring direct centralized review of raw data, maintaining privacy while achieving automated oversight.
3Quantity of substance
If high-dimensional raw data is used, then data richness is maintained, but feature importance clarity and outlier distinction deteriorate
Solution Approach 1:
The patent extracts and focuses on the most salient topological features from high-dimensional data using persistence diagrams and topological invariants. By computing topological signatures (such as Betti numbers, persistence values) that capture essential structural characteristics, the system distills the complexity of high-dimensional data into meaningful features that clearly indicate anomaly, making feature importance identifiable even in high-dimensional spaces.
Solution Approach 2:
The system transforms high-dimensional raw data into a different dimensional representation using topological data analysis. By converting data into topological features (persistence diagrams, barcode representations) that capture structural information, the patent creates a new feature space where anomaly detection becomes more straightforward, effectively managing the dimensionality challenge through a dimensional transformation that preserves essential data characteristics.
Data Source
AI summary
Systems and methods for detecting anomalous data updates in federated learning. In some aspects, the system receives a plurality of data updates. Each data update contains data in a first real-valued space. The system selects a first function to project the plurality of data updates into a second real-valued space. The system selects a second function to partition the second real-valued space into a plurality of sectors. The system generates a plurality of sector datasets associated with the plurality of sectors. The system processes the plurality of sector datasets to generate a relational data structure. The system determines outliers in the relational data structure corresponding to anomalous data updates.