Confederated MLOps With Affinity Grouping for Data-Scarce Federated Learning
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Solution Overview
Problem
Federated learning faces challenges in domains with data scarcity and data quality issues, particularly in automotive manufacturing and software-defined vehicles, where data cannot be easily shared due to privacy concerns and transferring data to central servers is costly.
Innovation Solution
A participatory Distributed Confederate Machine Learning Operations (MLOps) framework with Stochastic Optimization and affinity index-based selection of collaborating members, enabling group-based data sharing among nodes with similar features, using Generative AI for data augmentation, and hierarchical optimization to reduce communication overhead and improve convergence speed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If federated learning is used to enable collaborative ML without centralized training data, then data privacy and security are improved, but data scarcity at individual client nodes worsens
Solution Approach 1:
The system segments client nodes into groups based on feature similarity (affinity index), allowing localized data sharing within groups while maintaining privacy. This segmentation enables data scarcity to be addressed at the group level without compromising individual node privacy, as data remains distributed but collaborative within homogeneous groups.
Solution Approach 2:
The patent introduces an intermediary aggregation mechanism that facilitates controlled data sharing between client nodes. The system uses affinity index calculations and group-based aggregation to mediate data exchange, enabling clients to share data selectively with similar nodes while preserving overall data privacy and security.
2Manufacturing precision
If data sharing is enabled to address data scarcity, then model accuracy is improved, but communication overhead and data transfer costs increase
Solution Approach 1:
The system implements local quality by enabling data sharing only within groups of client nodes that have similar features (high affinity index). This localized approach ensures that data exchange occurs only when beneficial for model accuracy, avoiding unnecessary communication with dissimilar nodes and reducing overall communication overhead.
Solution Approach 2:
The patent performs preliminary affinity index calculations to identify suitable collaborating nodes before data sharing begins. This preliminary classification allows the system to pre-organize data exchange pathways, reducing communication overhead during actual data transfer by avoiding inefficient broad-based data sharing.
3Manufacturing precision
If group-based data sharing is implemented, then data quality is improved, but system complexity increases
Solution Approach 1:
The system uses parameter changes by calculating an affinity index based on feature similarity metrics to dynamically determine group memberships. This parameter-based approach automatically adapts to different data characteristics and can reconfigure groups as needed, managing system complexity through algorithmic automation rather than manual configuration.
Data Source
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AI summary
State of art techniques. A method and system for participatory Distributed Confederate Machine Learning Operations (MLOps) framework with Stochastic Optimization and affinity index-based selection of collaborating members is disclosed. , in accordance with some embodiments of the present disclosure. The MLOps framework addresses the gap in the space of federated learning by enabling or supporting data sharing within group having members with commonality. The commonality is defined based on an affinity index based grouping of members participating in collaborative learning. Even after data sharing, the data may still be insufficient, thus Time series based data augmentation techniques using Generative AI can be used to generate synthetic data for initial training iterations. The client and server/aggregator time allotment during the training of ML models is guided by stochastic gradient descent optimization (SDCA) enabling faster convergence with desirable ML accuracy.