Federated Object Detection With Few-Shot Enhancement and Weighted Aggregation
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Solution Overview
Problem
Existing federated object detection learning algorithms suffer from low global model accuracy and weak generalization ability, particularly in scenarios with heterogeneous sample distributions, leading to reduced performance and inadequate handling of few-shot categories.
Innovation Solution
A federated object detection method that enhances representation of few-shot categories through unbalance softmax functions and gradient renewal at the client level, combined with weighted aggregation based on sample distribution and count at the server level, optimizing the model training process under a cloud-edge-terminal environment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If federated learning is applied in object detection model training to protect client data privacy, then data privacy is protected and distributed collaboration is enabled, but the global model performance decreases when client data distribution is heterogeneous
Solution Approach 1:
The patent applies local quality by allowing each client to perform local data augmentation and feature extraction tailored to their specific data characteristics. Each client enhances their local representations independently before federated aggregation, ensuring that local data heterogeneity is addressed individually while maintaining global collaboration for privacy protection.
Solution Approach 2:
The patent combines multiple processing stages (local data augmentation, local feature extraction, federated aggregation) into a composite federated learning framework. This composite approach integrates different processing techniques to simultaneously achieve privacy protection and maintain high global model accuracy despite heterogeneous data distributions.
2Extent of automation
If existing federated object detection learning algorithms are used, then federated learning framework is implemented, but the global model accuracy is low and generalization ability is weak
Solution Approach 1:
The patent applies preliminary action by performing data augmentation and feature extraction at the client side before federated aggregation. This preprocessing prepares the data in advance to ensure higher quality inputs for the global model training, thereby improving both accuracy and generalization ability while maintaining automated federated learning implementation.
Solution Approach 2:
The patent introduces an intermediary processing stage at each client that transforms raw local data into enhanced feature representations before contributing to the global model. This intermediary step acts as a mediator that improves the quality of contributed information, leading to better global model accuracy without compromising the automated federated framework.
3Measurement precision
If representation enhancement and weighted aggregation are implemented, then global model accuracy and generalization ability are improved, but computational complexity increases
Solution Approach 1:
The patent segments the federated learning process into distinct modular stages: local data augmentation, local feature extraction, and federated weighted aggregation. This segmentation allows each component to be optimized independently, improving global model accuracy through representation enhancement while managing computational complexity through modular design that can be implemented incrementally.
Data Source
AI summary
A federated object detection learning method based on representation enhancement and weighted aggregation under cloud-edge-terminal environment comprises the steps of: 1) building a centralized federated learning framework under cloud-edge-terminal environment; 2) locally conducting representation enhancement training to strengthen model learning for few-shot category after receiving a model from the server at the client; 3) carrying out the weighted aggregation for client models in accordance with sample distribution to obtain the global model after receiving models from all clients at the server. With regard to the problem of existing federated object detection learning on low global model accuracy and weak generalization ability, the present invention can improve the accuracy and generalization ability of global object detection model.