Detecting model inversion attacks in federated learning

Abstract

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.