Federated Learning Model Screening for Inversion Attack Detection
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Solution Overview
Problem
In federated learning, user data can be compromised by model inversion attacks, where a malicious coordinating server manipulates initial model weights to extract user data during training, compromising privacy and resource efficiency.
Innovation Solution
A processing system analyzes initial machine learning models for signs of compromise by checking gradients of cost functions against thresholds, preventing training with compromised models and implementing corrective actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If federated learning is used to train models decentralized, then data privacy is improved, but the system becomes vulnerable to model inversion attacks
Solution Approach 1:
The system performs preliminary analysis of the initial machine learning model before training begins. By computing gradients of the cost function with respect to model parameters and checking if they are zero or near-zero, the system detects compromised models in advance, preventing data exposure before it can occur during the training process.
Solution Approach 2:
The gradient computation serves as an intermediary mechanism between the initial model and the training data. By analyzing the gradients as an intermediate step, the system can detect signs of model inversion attacks without directly exposing the training data, thus maintaining privacy while detecting threats.
2Reliability
If the system analyzes initial models for compromise before training, then security is improved, but processing time and computational resources increase
Solution Approach 1:
The system performs a partial analysis by computing gradients for only a subset of data items or using threshold-based stopping criteria. The analysis stops when the counter of non-zero gradients satisfies a threshold condition, avoiding exhaustive analysis of all possible data points while still providing adequate security detection.
3Measurement precision
If gradient computation is performed for every data item, then detection accuracy is improved, but computational complexity increases
Solution Approach 1:
The gradient computation serves multiple functions simultaneously: it is both a necessary step for model training (computing training gradients) and a detection mechanism (computing analysis gradients to check for model inversion attacks). This multi-functionality reduces overall computational complexity by reusing the same computation for dual purposes.
Data Source
AI summary
Examples described herein provide a method that includes receiving, by a processing system from a coordinating server, an initial machine learning model to be trained by the processing system using data associated with the processing system to generate a trained local machine learning model, the trained local machine learning model being one of a plurality of local machine learning models that the coordinating server uses to generate a global machine learning model using federated learning. The method further includes analyzing, by the processing system, the initial machine learning model to determine whether the initial machine learning model is a compromised model prior to training the initial machine learning model. The method further includes, responsive to determining that the initial machine learning model is a compromised model, implementing a corrective action to prevent training the initial machine learning model using the compromised model.