Forkable Federated Learning Model Pathways
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Solution Overview
Problem
Federated learning systems face inefficiencies due to limited computational speed, reliance on incomplete client data, and rigidity in training, which can lead to suboptimal model updates and missed valuable user data, as well as challenges in clustering clients while preserving privacy and accuracy.
Innovation Solution
The system clusters clients based on relevant attributes using a clustering algorithm, generates synthetic data to supplement incomplete datasets, and implements a forking mechanism for the global model to allow multiple versions to be trained simultaneously, enabling more comprehensive and efficient training while maintaining privacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If clients are treated as separate and distinct entities in federated learning, then privacy is maintained, but training efficiency is reduced due to inability to target minority classes
Solution Approach 1:
The patent segments clients into distinct groups (clusters) based on their data characteristics and training performance. This allows the system to treat different client groups differently - targeting minority classes specifically while maintaining the privacy benefits of federated learning. The segmentation is achieved through clustering algorithms that group clients with similar attributes together.
Solution Approach 2:
The patent applies local quality by customizing training approaches for different client clusters. Instead of uniform treatment, the system identifies minority class clients and applies enhanced training techniques specifically to those groups, while maintaining standard protocols for other clients. This localized optimization improves overall training efficiency without compromising privacy.
2Measurement precision
If clients with incomplete datasets are disregarded, then data quality is maintained, but valuable real user data is lost
Solution Approach 1:
The patent introduces synthetic data as an intermediary element that bridges the gap between incomplete client datasets and the requirements for comprehensive training. Synthetic data generators create artificial data samples that complement existing client data, allowing clients with incomplete datasets to still contribute valuable information to the global model without compromising data quality.
Solution Approach 2:
The system creates copies of data through synthetic data generation. Instead of requiring complete real user data from all clients, the patent generates synthetic representations that capture essential data characteristics. This copying approach allows clients with incomplete datasets to contribute to training while maintaining data quality standards.
3Adaptability or versatility
If a single version of the global model is used, then system simplicity is maintained, but the model is limited by local minima problems and lacks granular adjustment capability
Solution Approach 1:
The patent introduces dynamic model versioning where the global model can fork into multiple versions based on training needs. This dynamic approach allows the system to create specialized model variants for different client clusters or training objectives, then merge successful versions back into the global model. The dynamics enable flexible adaptation while maintaining manageable complexity through automated version control.
Solution Approach 2:
The system merges successful model versions and client updates into a unified global model. By aggregating the most effective parameters from multiple model versions and client contributions, the patent achieves both adaptability to different training scenarios and the simplicity of a single consolidated model. The merging process resolves local minima by combining diverse solutions.
Data Source
AI summary
Methods and systems are described for novel uses and/or improvements to federated learning. As one example, methods and systems are described for improving the applicability of federated learning across various applications and increasing the efficiency of training a global model through federated learning. As another example, methods and systems are described for ensuring comprehensive training data is available to models assigned by the federated learning server. Additionally, methods and systems are described for improving the rate of training a global model through federated learning.