Second-Order Differential Privacy Updates for Federated Learning
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Solution Overview
Problem
Existing federated learning methods face challenges in achieving efficient communication and privacy protection, particularly in 5G communication networks, due to high communication overhead and vulnerability to attacks like membership inference and model inversion, despite using differential privacy (DP) techniques.
Innovation Solution
Implementing a second-order differential privacy optimization method with model-sized communication, utilizing clipped gradients and noise addition at the client devices to ensure privacy and reduce communication overhead, while leveraging second-order information for faster convergence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If differential privacy techniques are used in federated learning, then privacy protection is improved, but communication overhead increases
Solution Approach 1:
The patent changes the parameters of the machine learning model updates by applying clipping to gradient norms and adding calibrated noise, transforming the update mechanism to achieve differential privacy while controlling communication overhead through parameter manipulation
Solution Approach 2:
The patent introduces an intermediary noise addition mechanism that acts as a mediator between the local model updates and the server, ensuring privacy protection through controlled information distortion while managing communication requirements
2Productivity
If standard federated learning methods are used, then communication efficiency is improved, but vulnerability to attacks like membership inference and model inversion increases
Solution Approach 1:
The patent applies preliminary anti-action by clipping gradient norms and adding noise before transmitting updates to the server, preemptively counteracting potential membership inference and model inversion attacks before they can occur
Solution Approach 2:
The patent converts the harmful effect of gradient leakage into a beneficial privacy protection mechanism by deliberately adding noise and clipping gradients, transforming what would be vulnerable information into protected updates that still enable learning
3Speed
If second-order optimization methods are used, then convergence speed is improved, but computational complexity increases
Solution Approach 1:
The patent applies partial second-order optimization by computing and utilizing only the necessary second-order information (Hessian matrix or its approximation) at key iterations rather than fully implementing second-order methods throughout, achieving faster convergence while managing computational complexity
Solution Approach 2:
The patent changes the optimization approach by incorporating second-order information (Hessian matrix or approximation) into the update mechanism, transforming the optimization process to achieve faster convergence rates while controlling computational requirements through parameter-based approaches
Data Source
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AI summary
An apparatus comprising: means for computing a non-differentially private update of an approximation of a global machine learning model; means for clipping and adding noise to the non-differentially private update of the approximation of the global machine learning model, for computing a differentially private update of the approximation of the global machine learning model; and means for sending, to a server, the differentially private update of the approximation of the global machine learning model.