On the limits of privacy in federated learning : personalization and adversarial analysis
Abstract
With the rapid adoption of Artificial Intelligence across diverse applications, concerns around data privacy have become increasingly prominent. Traditional centralized machine learning approaches require aggregating large volumes of user data at a central server, posing risks related to privacy, scalability, and regulatory compliance. Federated Learning has emerged as a decentralized alternative, enabling clients to train models locally while only sharing model updates with a central server. It addresses some of the concerns by enabling multiple participants to collaboratively train a shared model while keeping their raw data locally stored. However, data locality alone does not guarantee privacy. Information about local training records may still be revealed through model parameters, gradients, updates, or predictions.This makes FL vulnerable to privacy attacks such as model poisoning, data reconstruction and membership inference attacks. This thesis investigates these limitations in FL through two complementary perspectives: adversarial evaluation of the framework and personalization of privacy protection. In this thesis, we first examine the validity of various data anonymization techniques for facial images through the re-identification attacks. A weight-optimized record linkage method is developed to determine whether anonymized image records can be linked to their corresponding original records. The evaluation considers different adversarial settings, including cases…
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