Group Personalized Federated Learning Model Segmentation
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Solution Overview
Problem
Conventional federated learning frameworks face challenges in improving machine learning model performance for individual client devices due to limited training examples and heterogeneity in local data distributions, leading to suboptimal performance and potential overfitting.
Innovation Solution
The approach involves partitioning client devices into groups based on similar characteristics and model parameters, allowing for group-specific model fine-tuning using local data, enabling each group to leverage knowledge from others for personalized model enhancement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a single global model is trained for all client devices, then the training process is simple and efficient, but the model performance deteriorates for individual client devices due to limited training examples and data heterogeneity
Solution Approach 1:
The patent segments the homogeneous global model into multiple heterogeneous group-specific models. Client devices are partitioned into groups based on similar characteristics (e.g., demographics, device type, data distribution), and each group receives a dedicated model that is fine-tuned on the group's local data. This segmentation allows each model to be optimized for its specific group's data distribution, resolving the contradiction between training efficiency and individual performance.
Solution Approach 2:
The patent applies local quality by customizing model parameters for each group based on the specific characteristics and data distribution of that group. Instead of using a single uniform model for all clients, each group receives models fine-tuned on their local data, allowing the model to adapt to local data heterogeneity while maintaining overall system efficiency.
2Reliability
If additional training data is collected for each client device, then the model performance improves, but the data privacy requirements are violated and the system complexity increases
Solution Approach 1:
The patent merges the training data of multiple client devices within each group to create a pooled training dataset for group-specific models. By combining data from multiple devices while maintaining privacy through federated learning, the system achieves improved model performance without requiring each individual device to have large amounts of data, thus avoiding the complexity of implementing complex data aggregation systems.
Solution Approach 2:
The patent introduces group-specific models as intermediaries between the global model and individual client devices. These intermediary models serve as a buffer that allows data to be pooled at the group level rather than at the individual device level, maintaining privacy while enabling improved performance through aggregated data utilization.
3Reliability
If client devices are partitioned into groups for personalized training, then the model performance improves through knowledge sharing, but the system complexity and communication overhead increase
Solution Approach 1:
The patent segments client devices into groups based on similar characteristics and trains separate models for each group. This segmentation enables targeted knowledge sharing within groups where data distributions are similar, improving personalization performance while limiting the scope of communication overhead to only relevant groups rather than all devices.
Solution Approach 2:
The patent utilizes parameter changes by fine-tuning model parameters for each group based on the group's specific data distribution characteristics. This allows the system to capture group-specific patterns and improve personalization performance by adapting model parameters to local data characteristics without requiring complete system redesign.
Data Source
AI summary
A system and method facilitating group personalized federated learning are provided. The system may provide a global machine learning model, generated based on federated learning, to communication devices. The system may also receive model parameters from the communication devices based in part on the communication devices determining local training data generated by the communication devices implementing the global machine learning model. The system may also determine, based on the model parameters from the communication devices, groups of the communication devices and may assign a group specific machine learning model(s), among a plurality of group specific machine learning models, to the groups. The plurality of group specific machine learning models may be associated with the global machine learning model. The system may also provide respective group specific machine learning models, among the plurality of group specific machine learning models, to subsets of communication devices of the groups of the communication devices.