Federated Data Outlier Detection with Privacy-Preserving Calibration
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Solution Overview
Problem
Conventional artificial intelligence models are inadequate for hybrid data analytics due to compliance, security, and data privacy concerns in federated learning frameworks, making it difficult to identify outliers across multiple cloud environments without violating privacy constraints.
Innovation Solution
An outlier-aware federated learning system that dynamically updates outlierness scores of local data in a privacy-preserving manner, using artificial intelligence models to learn data signatures and adjust local and global notions of outliers, with a server recalibrating clients' outlierness measures through feedback signals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data is pooled across multiple cloud environments for centralized AI model training, then model training effectiveness is improved, but data privacy and security constraints are violated
Solution Approach 1:
The patent segments the centralized data pooling process into distributed federated learning across multiple client systems. Each client retains its data locally while contributing to global model training through shared model parameters, thus maintaining model training effectiveness while preserving data privacy constraints.
Solution Approach 2:
The patent introduces a federated learning server as an intermediary that coordinates model training without directly accessing client data. The server aggregates model updates from multiple clients and distributes refined model parameters, enabling effective model training while acting as a privacy-preserving mediator between distributed data sources.
2Measurement precision
If outlier detection is performed on individual client data in federated learning, then local outlier identification is improved, but global outlier consistency deteriorates due to lack of data sharing
Solution Approach 1:
The patent merges local outlier detection results from multiple client systems into a unified global outlier detection framework. By combining local outlier indicators and using federated aggregation techniques, the system achieves both accurate local outlier identification and consistent global outlier detection without requiring direct data sharing.
Solution Approach 2:
The patent implements a feedback mechanism where the federated learning server receives local outlier detection results from clients, aggregates them to identify global outliers, and sends calibration parameters back to clients. This feedback loop enables clients to refine their local outlier detection using global information while maintaining data privacy, thus achieving both local precision and global consistency.
3Reliability
If federated learning is used to preserve data privacy, then data security is improved, but the ability to detect outliers across cloud environments deteriorates
Solution Approach 1:
The patent uses the federated learning server as an intermediary to enable outlier detection across distributed cloud environments while preserving data security. The server aggregates outlier-related information from multiple clients and performs global outlier detection without accessing raw client data, thus maintaining data security while improving cross-environment outlier detection capability.
Solution Approach 2:
The patent replaces the mechanical approach of centralized data collection with an information-theoretic approach using federated aggregation of model parameters and outlier indicators. This substitution enables outlier detection across distributed environments by working with aggregated statistical information rather than raw data, thus maintaining data security while enhancing detection capability.
Data Source
AI summary
Methods, systems, and computer program products for automatically detecting outliers in federated data are provided herein. A computer-implemented method includes obtaining local outlier-related data from multiple client systems within a federated learning environment; detecting one or more federated learning environment-level outliers from at least a portion of the multiple client systems by processing at least a portion of the obtained local outlier-related data using one or more artificial intelligence models; determining at least one calibration parameter for detecting federated learning environment-level outliers based at least in part on the one or more detected federated learning environment-level outliers; and outputting the at least one determined calibration parameter to at least a portion of the multiple client systems within the federated learning environment.