Federated Learning under Attack: Improving Gradient Inversion for Batch of Images
Federated Learning (FL) has emerged as a machine learning approach able to preserve the privacy of user's data. Applying FL, clients train machine learning models on a local dataset and a central server aggregates the learned parameters coming from the clients, training a global machine learning model without sharing user's data. However, the state-of-the-art shows several approaches to promote attacks on FL systems. For instance, inverting or leaking gradient attacks can find, with high precision, the local dataset used during the training phase of the FL. This paper presents an approach, called Deep Leakage from Gradients with Feedback Blending (DLG-FB), which is able to improve the inverting gradient attack, considering the spatial correlation that typically exists in batches of images. The performed evaluation shows an improvement of 19.18% and 48,82% in terms of attack success rate and the number of iterations per attacked image, respectively.
Code (0)
등록된 구현이 없습니다.
Tasks
Federated LearningSimilar Papers 제목 키워드 기반
Cascading Gradient Inversion via LT-Code Inspired Peeling in Federated Learning
Federated learning shares model updates rather than raw data, yet these updates can be inverted to reconstruct the clients' training data. Analytic reconstruction attacks, which invert a gradient in closed form, degrade …
Federated LearningGI-SMN: Gradient Inversion Attack against Federated Learning without Prior Knowledge
Federated learning (FL) has emerged as a privacy-preserving machine learning approach where multiple parties share gradient information rather than original user data. Recent work has demonstrated that gradient inversion…
Federated LearningPrivacy PreservingAGIC: Approximate Gradient Inversion Attack on Federated Learning
Federated learning is a private-by-design distributed learning paradigm where clients train local models on their own data before a central server aggregates their local updates to compute a global model. Depending on th…
Federated LearningSPEAR:Exact Gradient Inversion of Batches in Federated Learning
Federated learning is a framework for collaborative machine learning where clients only share gradient updates and not their private data with a server. However, it was recently shown that gradient inversion attacks can …
Federated LearningGPUEnhancing Privacy against Inversion Attacks in Federated Learning by using Mixing Gradients Strategies
Federated learning reduces the risk of information leakage, but remains vulnerable to attacks. We investigate how several neural network design decisions can defend against gradients inversion attacks. We show that overl…
Federated Learning