Privacy-Preserving Federated Object Detection With Local-Output Distillation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing federated learning methods fail to ensure data privacy, particularly in object detection tasks, as they often lead to data leakage through direct or indirect means, compromising sensitive data such as medical records.
Innovation Solution
A method involving training an object detection model on a central dataset, distributing a preliminary model to local nodes, and generating a central model using average outputs from local models trained on public datasets, ensuring privacy by keeping private data local and using knowledge distillation techniques like Eigen-CAM and Grad-CAM to transfer knowledge efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If local data is directly transferred or model parameters are shared in federated learning, then knowledge from local data can be incorporated into the central model, but data leakage occurs compromising privacy of sensitive data
Solution Approach 1:
The patent introduces an intermediary mechanism where local models process data locally and only share processed outputs (predictions, attention maps, feature representations) with the central server, rather than sharing raw data or model parameters. This intermediary processing layer prevents direct data leakage while enabling knowledge transfer, as the central model learns from aggregated local insights without accessing sensitive local data.
Solution Approach 2:
The patent segments the federated learning process into distinct components: local data processing, local model training, and centralized aggregation. By dividing the system into local nodes that independently process data and a central node that aggregates results, the patent enables knowledge transfer while maintaining data privacy through architectural separation.
2Object-affected harmful factors
If private training datasets are kept local in federated learning, then data privacy is preserved, but the central model cannot directly learn from diverse local data distributions
Solution Approach 1:
The patent uses local models as intermediaries that reside at each local node and process private data locally. These local models act as mediators that extract knowledge from private datasets and transmit only processed information to the central server, enabling the central model to learn from diverse data distributions without direct access to private data.
Solution Approach 2:
The patent employs knowledge distillation where a central model learns to replicate the behavior of multiple local models by matching their outputs on a public dataset. This copying approach allows the central model to acquire knowledge from diverse local data distributions through output alignment rather than direct data access.
3Reliability
If knowledge distillation is performed using local model outputs on public datasets, then a reliable central model can be obtained, but additional computational steps are required
Solution Approach 1:
The patent performs preliminary local training at each node before central aggregation, where local models are pre-trained on private datasets and then used to generate training signals for the central model. This preliminary action prepares the system in advance, enabling more efficient central model training through knowledge distillation on public datasets.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
The invention concerns a method (20) for performing privacy-preserving federated learning, the method comprising the steps: - training (22) an object detection model based on a central training dataset (12) to obtain a preliminary model (30); - for each of N local nodes (8), training (24) a respective copy of the preliminary model based on a respective private training dataset (14), thereby obtaining a local model (32); - computing (26) an average model (34) as an average of N intermediate models (38), each depending on a respective local model (32); and - generating (28) a central model (36) based: • on the preliminary model (30) and on an average output of the average model (34) based on a predetermined public dataset (10) as input; or • on the average model (34) and a local output of each local model (32) based on the public dataset (10) as input.