Federated Learning Model Segmentation for Client-Specific Data Adaptation
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Solution Overview
Problem
Federated learning systems face issues due to data imbalance among client devices, leading to catastrophic forgetting and insufficient performance when applying models to individual client devices with specific data distributions, as existing methods do not effectively update parameters to account for diverse data sets.
Innovation Solution
The system divides the federated learning model into an extractor and a classifier, allowing client devices to intensively train the extractor using local data and maintain the classifier's parameter value, enabling each device to update and customize the model for its specific data distribution, thereby improving learning speed and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If federated learning is performed by centrally collecting models trained by each client device, then network costs are reduced and privacy is protected, but data imbalance among client devices causes catastrophic forgetting and insufficient performance
Solution Approach 1:
The patent segments the federated learning model into two distinct parameter sets: extractor parameters (shared across all clients) and classifier parameters (customized per client). This segmentation allows the system to maintain a unified global model structure while enabling client-specific adaptations, thereby resolving the contradiction between network efficiency and performance reliability.
Solution Approach 2:
The patent applies local quality by allowing each client device to train and maintain its own classifier parameters locally based on its specific data distribution, while sharing extractor parameters with the central server. This enables each client to have customized model components adapted to its local data characteristics, improving performance without compromising the federated learning framework's network efficiency and privacy protection benefits.
2Productivity
If a unified federated learning model is used across all client devices, then training efficiency is improved, but the model shows insufficient performance when applied to individual client devices with specific data distributions
Solution Approach 1:
The model is segmented into extractor and classifier components with different training strategies. Extractor parameters are trained centrally with high efficiency, while classifier parameters are trained locally to adapt to individual client data distributions. This segmentation maintains training efficiency for the shared components while enabling performance optimization for individual clients.
Solution Approach 2:
The patent introduces dynamic adaptability by allowing classifier parameters to be customized per client based on local data characteristics, while extractor parameters remain unified. This dynamic structure enables the model to adapt its behavior to different client environments, resolving the contradiction between unified training efficiency and individual performance requirements.
3Reliability
If each client device trains the entire federated learning model locally, then individual client performance is improved, but network costs and computational resources increase significantly
Solution Approach 1:
By segmenting the model into extractor and classifier parameters, the patent enables each client to train only its local classifier parameters, which is computationally less intensive than training the entire model. This reduces the computational burden and network communication overhead while still achieving client-specific performance optimization.
Solution Approach 2:
Each client device performs self-service by training its own classifier parameters locally using its own data, without requiring extensive central server resources or large-scale network communication. This self-service approach improves individual client performance while minimizing network costs and computational resource requirements.
Data Source
AI summary
Proposed is a federated learning system. The federated learning system may comprise: a central server configured to transmit a first parameter of an extractor in a federated learning model including the extractor and a classifier to each of a plurality of client devices, and receive a plurality of first parameters learned from the plurality of client devices to update the federated learning model; and the plurality of client devices configured to train each of the plurality of the first parameters of the federated learning model using a training data set stored in each of the plurality of client devices while maintaining a value of a second parameter value of the classifier in the federated learning model, and to transmit each of the plurality of the trained first parameters to the central server.