Intermediate Model Privacy Violation Detection in Federated Learning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Federated learning systems are vulnerable to privacy violations due to the potential leakage of training data through intermediate models, which can lead to unauthorized access and breaches of privacy laws or regulations.
Innovation Solution
Implement a method and apparatus in wireless transmit/receive units (WTRUs) and network entities to detect privacy violations in federated learning models by using client-side and network-side privacy violation engines, employing white box and black box approaches to analyze intermediate models and training data against privacy policies and regulations, ensuring compliance before model integration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If federated learning intermediate models are shared across multiple devices, then model training efficiency and collaboration are improved, but privacy violation risks and data leakage vulnerabilities increase
Solution Approach 1:
The patent implements privacy violation detection mechanisms that operate in advance before intermediate models are shared or trained. Network entities and client devices perform detection on received intermediate models before using them in federated learning processes, preventing privacy violations from occurring rather than detecting them after the fact.
Solution Approach 2:
The patent introduces network entities as intermediary components that mediate between client devices and the federated learning process. These network entities receive, detect, and validate intermediate models before they are distributed to other devices, acting as a trusted intermediary that ensures privacy compliance while enabling model sharing.
2Reliability
If privacy violation detection mechanisms are implemented in federated learning systems, then privacy protection is improved, but system complexity and computational overhead increase
Solution Approach 1:
The patent divides privacy violation detection functionality into separate, modular components: client-side detection mechanisms on user devices, network entity detection capabilities, and server-side validation. This segmentation allows each component to perform specialized detection tasks independently, reducing overall system complexity while maintaining comprehensive privacy protection.
Solution Approach 2:
The patent enables client devices to perform self-detection of privacy violations in intermediate models they receive. Each device has embedded detection capabilities that allow it to autonomously validate models without requiring constant external verification, reducing the burden on central servers and simplifying the overall system architecture.
3Measurement precision
If comprehensive privacy violation detection is performed on all intermediate models, then detection accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The patent implements a tiered detection approach where not all intermediate models undergo the same level of comprehensive analysis. Instead, detection intensity is adjusted based on risk factors, model sources, and contextual information. High-risk models receive exhaustive detection while lower-risk models undergo streamlined validation, reducing overall processing time while maintaining detection accuracy for critical cases.
Solution Approach 2:
The patent incorporates feedback mechanisms where detection results from previous intermediate models inform the detection process for subsequent models. When privacy violations are detected, the system learns from these patterns and adjusts detection parameters, allowing for more efficient processing of future models while maintaining or improving detection accuracy through adaptive validation.
Data Source
AI summary
Methods, protocols, systems and apparatus for the detection of violations of privacy rules for intermediate models in federated learning applications are described. One method may include receiving, from a network entity, a federated learning intermediate model, training the federated learning intermediate model using a training data set unique to the WTRU to generate a trained federated learning intermediate model, and determining, based on one more rules associated with a location of the WTRU, whether any one or more of the trained federated learning intermediate model and the training data set indicate one or more privacy violations. Based on whether any one or more of the trained federated learning intermediate model and the training data set indicate one or more privacy violations, the method may include transmitting a message or information to the network entity.