Federated Model Validation with Policy-Based 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 and contamination, especially in applications involving sensitive information.
Innovation Solution
Implement a policy service in the federated learning framework that tests models for vulnerabilities using 'brute force' trials and numerical simulations, applies noise to gradients, and compares training parameters to input data to prevent accidental leakage, using Euclidean loss functions and continuous metrics to enhance privacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If model parameters are shared in federated learning, then model training efficiency is improved, but data privacy is compromised due to inversion attacks
Solution Approach 1:
The patent introduces an intermediate verification layer between model parameter sharing and data exposure. The policy service acts as a mediator that intercepts model parameters, performs inversion resistance testing, and validates privacy compliance before allowing parameter updates to proceed, thus enabling efficient federated learning while preventing data leakage
Solution Approach 2:
The patent applies preliminary privacy verification by testing model parameters for inversion resistance before they are shared or updated in the federated learning process. The policy service conducts brute force trials and numerical simulations in advance to ensure parameters cannot be inverted to expose underlying data, preventing privacy breaches before they occur
2Reliability
If privacy verification testing is implemented, then data security is improved, but system complexity increases
Solution Approach 1:
The patent implements self-service privacy verification where each client system's policy service independently tests its own model parameters for inversion resistance. This distributed approach eliminates the need for a centralized complex verification system, as each node performs self-validation before participating in federated learning, maintaining security while managing complexity locally
3Reliability
If brute force trials and numerical simulations are used for testing, then inversion resistance is improved, but computational overhead increases
Solution Approach 1:
The patent applies partial action by performing a limited number of brute force trials and numerical simulations rather than exhaustive testing. The policy service conducts sufficient verification to ensure inversion resistance while stopping before full computational exhaustion, achieving adequate security protection with manageable computational overhead
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.