Confederated MLOps Grouping for Privacy-Preserving Data Scarcity
Find Innovative SolutionsGenerate Solutions
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 the cloud 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 data sharing within groups of similar features through intra- and inter-group aggregators, using techniques like Generative AI for data augmentation and hierarchical optimization to reduce communication overhead and improve convergence speed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If federated learning is used to enable collaborative ML without centralized training data, then data privacy and security are improved, but data scarcity and data quality issues worsen
Solution Approach 1:
The patent segments client nodes into groups based on feature similarity (affinity index), enabling selective data sharing within homogeneous groups rather than universal sharing. This segmentation allows data to be shared only where beneficial, addressing data scarcity in specific domains while maintaining privacy through localized collaboration.
Solution Approach 2:
The patent introduces an intermediary aggregation mechanism that coordinates data sharing between client nodes and the central server. The intermediary manages the balance between enabling data sharing to improve quality and maintaining privacy constraints, acting as a mediator that enables collaborative learning without centralized data collection.
2Measurement precision
If data sharing is enabled to address data scarcity, then model accuracy is improved, but communication overhead and training time increase
Solution Approach 1:
The patent applies local quality by enabling data sharing and aggregation only within groups of client nodes that have similar features and data characteristics. This localized approach ensures that data sharing improves model accuracy for specific domains while minimizing unnecessary communication and computation across heterogeneous nodes, thereby reducing overall training time.
3Productivity
If client nodes are grouped based on feature similarity, then data sharing effectiveness is improved, but system complexity increases
Solution Approach 1:
The patent uses parameter changes by computing an affinity index based on feature space distances to determine group memberships. This quantitative approach to grouping simplifies the complexity management by providing a clear, measurable criterion for forming groups, making the system more manageable despite the added complexity of grouping and hierarchical aggregation mechanisms.
Data Source
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.