Federated Learning Client Clustering and Model Forking
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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 client clustering and privacy preservation.
Innovation Solution
Implementing client clustering based on attributes using a data profiler, generating synthetic data to augment incomplete datasets, and integrating a forking mechanism to allow multiple versions of the global model for simultaneous training, while maintaining privacy through local data processing and secure data transmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the same global model is sent to all clients and responses are interpreted equally, then system simplicity is maintained, but training efficiency is not maximized
Solution Approach 1:
The patent segments clients into different clusters based on their data characteristics, device attributes, and performance metrics. Each cluster receives customized training configurations and model versions, allowing differential treatment of clients to maximize training efficiency while maintaining manageable system complexity through automated clustering algorithms.
2Manufacturing precision
If clients with incomplete datasets are excluded from training, then data quality is maintained, but valuable real user data is lost
Solution Approach 1:
The patent creates synthetic data copies that mimic the statistical properties and distributions of incomplete client datasets. These synthetic data copies are used to augment training, allowing clients with incomplete data to participate in training while maintaining model training quality through the use of generated data that preserves the characteristics of missing information.
3Adaptability or versatility
If a single version of the global model is used for training, then system simplicity is maintained, but the system is restricted and cannot make granular adjustments
Solution Approach 1:
The patent implements a dynamic model versioning system where multiple versions of the global model can coexist and be assigned to different client clusters based on their specific needs and performance characteristics. This allows the system to adaptively select and update model versions for different clusters, providing granular control and flexibility while managing complexity through automated version selection and deployment mechanisms.
4Measurement precision
If client attributes are transmitted for clustering, then clustering accuracy is improved, but client privacy may be compromised
Solution Approach 1:
The patent introduces an intermediary clustering mechanism that processes client attributes through encrypted or anonymized representations. The clustering algorithm operates on transformed attribute data that preserves the necessary information for accurate clustering while removing or obscuring personally identifiable information, thus maintaining clustering accuracy without compromising client privacy.
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.