Federated Model Validation for Privacy Leakage Detection
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Solution Overview
Problem
Federated learning systems face privacy vulnerabilities as model parameters can be inverted to expose underlying data, risking data leakage among clients and servers.
Innovation Solution
Implement a policy service in the federated learning framework that tests models for vulnerabilities using 'brute force' trials and numerical simulations, applies Euclidean loss functions, and adds noise to gradients to enhance privacy, ensuring data protection through continuous metrics like image variation distance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If model parameters are shared in federated learning, then collaborative model training is enabled, but data privacy is compromised as parameters can be inverted to expose underlying data
Solution Approach 1:
The patent introduces an intermediary verification system that acts as a mediator between model parameter sharing and privacy protection. The policy service and validation module serve as intermediaries that verify model parameters before they are shared, ensuring that collaborative training can proceed while preventing data leakage through inversion attacks.
Solution Approach 2:
The patent applies preliminary action by performing validation and verification of model parameters before they are shared in the federated learning process. The policy service checks parameters in advance to ensure they meet privacy requirements, preventing potential data leakage before it can occur during parameter sharing.
2Object-affected harmful factors
If validation and verification processes are implemented, then data privacy is protected, but system complexity increases
Solution Approach 1:
The patent segments the validation and verification process into distinct modular components: a policy service for defining validation rules, a validation module for executing checks, and a verification mechanism for confirming parameter integrity. This segmentation makes the complex privacy protection system more manageable and implementable.
3Measurement precision
If brute force trials and numerical simulations are used for testing, then vulnerability detection accuracy is improved, but computational resources and time are consumed
Solution Approach 1:
The patent applies partial action by implementing a tiered validation approach where not all model parameters undergo the most computationally intensive brute force trials. Instead, the system performs preliminary checks on all parameters and applies more rigorous numerical simulations only to parameters that fail initial validation, reducing overall testing time while maintaining detection accuracy.
Data Source
AI summary
Systems and methods for federated model validation and data verification are disclosed. A method may include: (1) receiving, by a local computer program executed by client system, a federated machine learning model from a federated model server; (2) testing, by the local computer program and using a policy service, the federated machine learning model for vulnerabilities to attacks; (3) accepting, by the local computer program, the federated machine learning model in response to the federated machine learning model passing the testing; (4) training, by the local computer program, the federated machine learning model using input data comprising local data and outputting training parameters; (5) identifying, by the local computer program using the policy service, accidental leakage and/or contamination by comparing the training parameters to the input data; and (6) providing, by the local computer program, the training parameters to the federated model server.