Do Gradient Inversion Attacks Make Federated Learning Unsafe?
| Autor: | Ali Hatamizadeh, Hongxu Yin, Pavlo Molchanov, Andriy Myronenko, Wenqi Li, Prerna Dogra, Andrew Feng, Mona G. Flores, Jan Kautz, Daguang Xu, Holger R. Roth |
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| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Computer Science - Cryptography and Security Radiological and Ultrasound Technology Computer Science - Distributed Parallel and Cluster Computing Computer Vision and Pattern Recognition (cs.CV) Computer Science - Computer Vision and Pattern Recognition Distributed Parallel and Cluster Computing (cs.DC) Electrical and Electronic Engineering Cryptography and Security (cs.CR) Software Machine Learning (cs.LG) Computer Science Applications |
| Popis: | Federated learning (FL) allows the collaborative training of AI models without needing to share raw data. This capability makes it especially interesting for healthcare applications where patient and data privacy is of utmost concern. However, recent works on the inversion of deep neural networks from model gradients raised concerns about the security of FL in preventing the leakage of training data. In this work, we show that these attacks presented in the literature are impractical in FL use-cases where the clients' training involves updating the Batch Normalization (BN) statistics and provide a new baseline attack that works for such scenarios. Furthermore, we present new ways to measure and visualize potential data leakage in FL. Our work is a step towards establishing reproducible methods of measuring data leakage in FL and could help determine the optimal tradeoffs between privacy-preserving techniques, such as differential privacy, and model accuracy based on quantifiable metrics. Code is available at https://nvidia.github.io/NVFlare/research/quantifying-data-leakage. Revised version; Accepted to IEEE Transactions on Medical Imaging; Improved and reformatted version of https://www.researchsquare.com/article/rs-1147182/v2; Added NVFlare reference |
| Databáze: | OpenAIRE |
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