Federated Learning Risk Audits for Adaptive Parameter Privacy
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Solution Overview
Problem
Federated learning systems are vulnerable to attacks from malicious client devices that can poison and bias the global artificial intelligence model, leading to data reconstruction and other security threats, and existing methods like static noise injection and encrypted communications are inefficient or resource-intensive.
Innovation Solution
Implementing electronic risk audits to assess client devices for vulnerabilities and applying parameter privacy adjustment methods based on risk scores to protect the global model, including data reconstruction, inference, and poisoning attacks, with iterative adjustments to balance utility and risk.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If static noise injection is used to prevent data reconstruction attacks, then security is improved, but model performance decreases
Solution Approach 1:
The patent applies different privacy adjustment methods based on the risk level of individual client devices. High-risk devices receive stronger privacy adjustments (more aggressive parameter modifications) while low-risk devices receive minimal or no adjustments. This local differentiation allows the system to maintain strong security where needed while preserving model performance where risk is low, resolving the contradiction between uniform security measures and performance maintenance.
Solution Approach 2:
The patent dynamically changes the parameters of privacy adjustment methods based on risk audit results. Instead of applying fixed static noise injection, the system modifies parameters such as the degree of parameter modification, type of adjustment method, and intensity level based on the assessed risk score of each client device. This dynamic parameter adjustment allows optimization of both security and performance by adapting the strength of protection to the actual threat level.
2Reliability
If encrypted communications are used to protect data, then security is improved, but scalability to large number of client devices deteriorates
Solution Approach 1:
The patent extracts the security function from traditional encrypted communications and relocates it to a risk-based parameter adjustment mechanism. Instead of relying on cryptographic encryption for all communications, the system extracts security protection to targeted parameter modifications applied only to parameters from high-risk devices after risk audits. This extraction allows the system to scale efficiently by applying lightweight parameter adjustments rather than heavy cryptographic operations to all device communications.
3Reliability
If Trusted Execution Environments are used for secure code execution, then security is improved, but resource requirements and communication complexity increase
Solution Approach 1:
The patent replaces complex, resource-intensive Trusted Execution Environments with simpler, lightweight risk audit mechanisms. Instead of deploying TEEs that require specialized hardware and complex communication protocols, the system uses computationally lightweight risk audits that can be performed on standard devices. The security function is achieved through disposable parameter adjustment operations rather than persistent TEE infrastructure, significantly reducing resource requirements and communication complexity while maintaining security effectiveness.
Data Source
AI summary
A computing device, that is configured to configure a global machine learning model, performs respective electronic risk audits of client devices configured to train respective local machine learning models that correspond to a global machine learning model. Based on respective electronic risk scores of one or more of the client devices, determined via the respective electronic risk audits, the computing device implements one or more parameter privacy adjustment methods on respective parameters received from the client devices prior to using the respective parameters to configure the global machine learning model, wherein respective client devices determined to have higher electronic risk scores have more of the parameter privacy adjustment methods applied than other respective client devices determined to have lower electronic risk scores. The computing device provides, to the client devices, the global machine learning model configured according to the respective parameters as adjusted.