Federated Learning Outlier Detection for Global Model Quality
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Solution Overview
Problem
Current federated learning systems face challenges in identifying and addressing underperforming clients due to lack of immediate insight into performance issues, leading to time-consuming manual inspections and potential exclusion of valuable client data, which affects the development of a dynamic and effective global model.
Innovation Solution
A method that generates and trains AI-based models on clients, records model and data properties, and uses a correlation matrix to identify outlier values, providing actionable insights to clients for improving their performance and maintaining data contribution to the global model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual inspection is performed to identify underperforming clients, then the root cause of performance issues can be identified, but the process becomes very time-consuming and does not allow for timely investigation
Solution Approach 1:
The patent replaces manual mechanical inspection with an automated computer-implemented system that uses machine learning models to analyze client performance data, sensor configurations, and environmental factors. The system automatically identifies underperforming clients and determines root causes through algorithmic analysis rather than human inspection, thereby eliminating time loss while maintaining identification accuracy.
Solution Approach 2:
The patent introduces an intermediary automated analysis system that acts as a mediator between raw client performance data and actionable insights. This intermediary system processes data through correlation matrices and machine learning models to identify performance issues and their causes, freeing investigators from direct manual inspection while preserving the ability to identify root causes accurately.
2Reliability
If below-average clients are excluded from federated learning to improve overall model performance, then the global model quality improves, but customer-specific training information is lost and the system becomes less dynamic
Solution Approach 1:
The patent applies local quality by enabling differentiated treatment of clients based on their specific performance characteristics and outlier properties. Instead of uniform exclusion, the system identifies specific local issues (such as sensor misconfiguration or environmental changes) for individual clients and provides targeted recommendations, allowing each client to improve their local model quality while remaining part of the federated system.
Solution Approach 2:
The patent implements a feedback mechanism where the system continuously monitors client performance, identifies outliers through correlation analysis, and provides actionable recommendations to clients. This feedback loop allows underperforming clients to correct their issues and improve over time, maintaining their inclusion in the federated learning process while preserving overall model quality through continuous improvement rather than exclusion.
3Adaptability or versatility
If the federated learning system accepts all client models including low-performing ones, then client participation is maintained, but the global model performance deteriorates due to acceptance of inferior models
Solution Approach 1:
The patent applies preliminary action by performing automated outlier detection and root cause analysis on client models before they are fully integrated into the global federated model. The system proactively identifies performance issues and provides recommendations to clients ahead of time, allowing them to correct problems before their models significantly degrade global performance, thus maintaining participation while protecting model quality.
Solution Approach 2:
The patent uses feedback mechanisms to continuously monitor client model performance and provide targeted recommendations to underperforming clients. This ongoing feedback allows the system to maintain high client participation while actively managing model quality through continuous improvement guidance, preventing inferior models from degrading the global model.
Data Source
Figure 1~2g
AI summary
A computer-implemented method for operating a technical device with a federated learning-based model, comprising the following steps: a) generating a first model of the first client and at least one second model of at least one second client, b) capturing and providing model properties, data properties, and corresponding metrics of the first client and at least one second client and their associated technical devices to the server, c) training the first model of the first client and at least one second model of at least one second client, and transmitting the results to the server, d) generating and training a global model, and providing the global model to the first and at least one second client, e) determining a correlation matrix for the global model using the model properties.a) Determining the data properties and corresponding metrics of the first and at least one second client and their associated technical devices by applying a correlation function; b) Determining at least one outlier value of the correlation matrix, which lies outside a predefined range of values, and determining the outlier property associated with the at least one outlier value, which influences the correlation in the previous step beyond a predefined measure, as well as the associated client; c) Transmitting the outlier property determined in the previous step to the associated client.