Federated Learning without Revealing the Decision Boundaries

TL;DR

This paper highlights privacy risks in federated learning where mixed images reveal decision boundaries, and proposes encrypting images with hidden decryption modules to prevent exposure of sensitive information.

Contribution

It introduces a novel encryption-based method with hidden decryption modules to protect training images and decision boundaries in federated learning.

Findings

Mixed images reveal decision boundaries, risking model privacy.

Encrypted images prevent entity from accessing training data.

Proposed method enhances privacy by hiding decision boundaries.

Abstract

We consider the recent privacy preserving methods that train the models not on original images, but on mixed images that look like noise and hard to trace back to the original images. We explain that those mixed images will be samples on the decision boundaries of the trained model, and although such methods successfully hide the contents of images from the entity in charge of federated learning, they provide crucial information to that entity about the decision boundaries of the trained model. Once the entity has exact samples on the decision boundaries of the model, they may use it for effective adversarial attacks on the model during training and/or afterwards. If we have to hide our images from that entity, how can we trust them with the decision boundaries of our model? As a remedy, we propose a method to encrypt the images, and have a decryption module hidden inside the model. The entity in charge of federated learning will only have access to a set of complex-valued coefficients, but the model will first decrypt the images and then put them through the convolutional layers. This way, the entity will not see the training images and they will not know the location of the decision boundaries of the model.