Federated Learning Model Segmentation for Local Accuracy
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Solution Overview
Problem
Conventional federated learning frameworks face issues with optimal model performance due to environmental variance, feature imbalance, and label differences across client devices, leading to sub-optimal global models that diverge when applied locally, resulting in poor accuracy and generalization.
Innovation Solution
The approach separates the neural network model into common layers for feature extraction and client-specific layers for classification, allowing each client device to train and update the model locally while sharing only the common layers with the server, ensuring both global and local accuracy and generalization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a centralized training approach is used to create a single global model, then the model can achieve generalization across different environments, but the model performance deteriorates when applied to specific local datasets due to environmental variance and feature imbalance
Solution Approach 1:
The neural network model is segmented into two distinct parts: a global backbone model that is trained centrally and shared across all clients, and local client-specific models that are trained independently on each client's local data. This segmentation allows the global backbone to capture universal patterns while local models adapt to environment-specific variations, resolving the contradiction between generalization and local accuracy.
Solution Approach 2:
Each client device trains its own local model using its specific local dataset, allowing the model to be optimized for local environmental characteristics such as region-specific applications, protocols, and data distributions. This local quality approach enables each client to achieve high accuracy for its specific environment while still benefiting from the global backbone model.
2Productivity
If the complete model including all layers is shared and updated across all client devices, then the model can be optimized using data from multiple sources, but the data transfer volume increases and data privacy is compromised
Solution Approach 1:
Only the global backbone model parameters are extracted and shared with clients, while the local client-specific model parameters remain private to each client. This extraction approach allows efficient model optimization using aggregated data from multiple sources without requiring transfer of complete models or raw data, thus reducing data transfer volume and maintaining data privacy.
Solution Approach 2:
A server acts as an intermediary that receives model updates from multiple clients, aggregates the gradients or parameters, and distributes the updated global backbone model back to clients. This intermediary approach enables collaborative optimization using data from multiple sources without direct data sharing between clients, reducing data transfer volume and preserving privacy.
3Adaptability or versatility
If the complete model is updated at each client device, then the model can adapt to local data distributions, but the communication overhead and training time increase
Solution Approach 1:
The model is segmented such that only the global backbone parameters are updated and communicated between clients and server, while local models are trained independently without requiring full model synchronization. This segmentation significantly reduces communication overhead and training time while still enabling adaptation to local data distributions through the local client-specific models.
Solution Approach 2:
Instead of updating the complete model at each client device, only the necessary global backbone parameters are updated and shared. This partial action approach reduces communication overhead and training time while maintaining the essential adaptability to local data distributions through the local models that utilize the shared backbone.
Data Source
AI summary
A client computing device and a server computing device for federated machine learning. The client computing device is configured to receive a model comprising a set of common layers and a set of client-specific layers from the server computing device. After a training at the client computing device, the set of common layers and the set of client-specific layers are both updated. The set of updated common layers is sent to the server computing device, and the set of updated client-specific layers is stored at the client computing device. The server computing device is configured to receive multiple sets of updated common layers from different client computing devices.