Using Ml to Protect Ml: Defending Collaborative Privacy-Preserving Model Training

Overview

In machine learning applications using sensitive, distributed big data, i.e., medical data, voting records, etc., it must be assured that model training data cannot be recovered or exposed publicly. Currently, this inhibits wide-scale analysis of sensitive data across research institutions and industry partners. Next-generation models must be trained collectively, introducing a new set of security risks to the confidentiality of sensitive data. These security risks, such as model inversion, reconstruction attacks, etc., allow adversaries to extract the sensitive data on which models have been trained. To address this issue, differentially private training architectures, such as SplitFed Learning, have been introduced; these architectures keep sensitive data on-site and secure while training the model using information from the public gradients and following a distributed machine learning computing pattern. Still, they do not prevent the intrusion of malicious clients who can send incorrect data to collective training servers to leak parts of the sensitive data on which the model is trained. This paper addresses this vulnerability, developing robust detection mechanisms against adversaries who insert less than 1/3 of malicious clients into training and attempt to reveal sensitive data. Specifically, we propose a novel reinforcement learning machine-learning-based solution that can consistently defend against fixed and variable-based distance attacks from malicious clients and actively train itself to recognize malicious clients in numerous public model training scenarios. We then perform tests on publicly available datasets using edge devices and demonstrate the resilience of our approach against state-of-the-art attacks.

Competition history

• AJAS 2026 Category not listed

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