Semantic Segmentation Model Aggregation with Differential Privacy
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Solution Overview
Problem
Federated learning for semantic segmentation models faces challenges in statistical heterogeneity, communication efficiency, and privacy preservation, particularly with large models that incur significant communication overhead and vulnerability to adversarial attacks.
Innovation Solution
An adaptive regularized weight aggregation approach is implemented, using controlled noise and differential privacy to aggregate semantic segmentation models across multiple collaborators, optimizing collaborator selection, data compression, and privacy protection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If large deep learning models are used to improve performance, then model accuracy is improved, but communication overhead increases significantly
Solution Approach 1:
The patent segments the model training process by dividing collaborators into batches and processing model updates in stages. Each collaborator processes a subset of model parameters locally, and only the essential update information is transmitted to the server, reducing communication overhead while maintaining accuracy improvements from large models.
Solution Approach 2:
The patent extracts only the necessary model update information from the full model parameters. Instead of transmitting complete large model weights, the system extracts and transmits only the differential updates or compressed representations of changes, significantly reducing communication bandwidth requirements.
2Adaptability or versatility
If Federated Averaging is used to aggregate models, then statistical heterogeneity is addressed, but weight divergence remains due to skewed data distributions
Solution Approach 1:
The patent introduces dynamic weighting mechanisms that adaptively adjust the importance of different collaborators' model updates based on their data distribution characteristics. The system dynamically modifies aggregation weights to compensate for skewed data distributions, preventing weight divergence while maintaining flexibility across heterogeneous datasets.
Solution Approach 2:
The patent changes the aggregation parameters dynamically during the training process. Instead of using fixed equal weights, the system adjusts weighting parameters based on observed data distribution characteristics and model convergence progress, optimizing the balance between handling heterogeneity and ensuring weight convergence.
3Productivity
If model updates are transmitted to the server for aggregation, then collaborative learning is enabled, but privacy security is compromised due to vulnerability to adversarial attacks
Solution Approach 1:
The patent introduces secure aggregation protocols as intermediaries between collaborators and the server. These protocols enable model updates to be transmitted and aggregated while maintaining privacy through mechanisms like secure multi-party computation or differential privacy, preventing direct exposure of raw data to the server or other collaborators.
Solution Approach 2:
The patent applies privacy protection measures in advance before model updates are transmitted. By incorporating differential privacy noise or secure computation layers beforehand, the system cushions against potential adversarial attacks, ensuring that even if transmission is intercepted or analyzed, sensitive information remains protected.
Data Source
AI summary
A method and a system for aggregation of semantic segmentation models from multiple collaborators includes selecting, in batches, at least two collaborators from the multiple collaborators. The method further includes receiving corresponding semantic segmentation model information from each of the at least two collaborators. The method further includes assigning respective weight parameters to the semantic segmentation model information from each of the at least two collaborators. The method further includes adding controlled noise to the weight parameters for the semantic segmentation model information from each of the at least two collaborators. The method further includes generating an aggregated model based on the semantic segmentation model information using the weight parameters with the noise added thereto from each of the at least two collaborators.