Absolute Variation Distance: an Inversion Attack Evaluation Metric for Federated Learning

Georgios Papadopoulos, yash satsangi, Shaltiel Eloul, Marco Pistoia

Keywords: federated learning, similarity metrics, inversion attacks

Abstract: Federated Learning (FL) has emerged as a pivotal approach for training models on decentralized data sources by sharing only model gradients. However, the shared gradients in FL are susceptible to inversion attacks which can expose sensitive information. While several defense and attack strategies have been proposed, their effectiveness is often evaluated using metrics that may not necessarily reflect the success rate of an attack or information retrieval, especially in the context of multidimensional data such as images. Traditional metrics like the Structural Similarity Index (SSIM), Peak Signal-to-Noise Ratio (PSNR), and Mean Squared Error (MSE) are typically used as lightweight metrics, assume only pixel-wise comparison, but fail to consider the semantic context of the recovered data. This paper introduces the Absolute Variation Distance (AVD), a lightweight metric derived from total variation, to assess data recovery and information leakage in FL. Unlike traditional metrics, AVD offers a continuous measure for extracting information in noisy images and aligns closely with human perception. Our results are combined with a user experience survey demonstrate that AVD provides a more accurate and consistent measure of data recovery. It also matches the accuracy of the more costly and complex Neural Network based metric, the Learned Perceptual Image Patch Similarity (LPIPS). Hence it offers an effective tool for automatic evaluation of data security in Federation and a reliable way of studying defence and inversion attacks strategies in FL.

Student Author Indication: Yes

Submission Number: 53