Conference Paper/Proceeding/Abstract 491 views 4 downloads
Gradients Stand-in for Defending Deep Leakage in Federated Learning
2024 International Conference on Computing in Natural Sciences, Biomedicine and Engineering (COMCONF), Pages: 53 - 64
Swansea University Authors:
Freya Hu, Hans Ren, Chen Hu, Yiming Li, Xianghua Xie
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DOI (Published version): 10.1109/comconf63340.2024.00017
Abstract
Federated Learning (FL) has become a cornerstone of privacy protection, shifting the paradigm towards localizing sensitive data while only sending model gradients to a central server. This strategy is designed to reinforce privacy protections and minimize the vulnerabilities inherent in centralized...
| Published in: | 2024 International Conference on Computing in Natural Sciences, Biomedicine and Engineering (COMCONF) |
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| ISBN: | 979-8-3503-5336-5 979-8-3503-5335-8 |
| Published: |
IEEE
2024
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa66608 |
| Abstract: |
Federated Learning (FL) has become a cornerstone of privacy protection, shifting the paradigm towards localizing sensitive data while only sending model gradients to a central server. This strategy is designed to reinforce privacy protections and minimize the vulnerabilities inherent in centralized data storage systems. Despite its innovative approach, recent empirical studies have highlighted potential weaknesses in FL, notably regarding the exchange of gradients. In response, this studyintroduces a novel, efficacious method aimed at safeguarding against gradient leakage, namely, “AdaDefense”. Following the idea that model convergence can be achieved by using differenttypes of optimization methods, we suggest using a local standin rather than the actual local gradient for global gradient aggregation on the central server. This proposed approach not only effectively prevents gradient leakage, but also ensures that the overall performance of the model remains largely unaffected. Delving into the theoretical dimensions, we explore how gradients may inadvertently leak private information and present a theoretical framework supporting the efficacy of our proposed method. Extensive empirical tests, supported by popular benchmark experiments,validate that our approach maintains model integrity and is robust against gradient leakage, marking an important step in our pursuit of safe and efficient FL. |
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| Keywords: |
Data privacy, Federated learning, Distance learning, Optimization methods, Distributed databases, Vectors, Robustness, Servers, Protection, Image reconstruction |
| College: |
Faculty of Science and Engineering |
| Start Page: |
53 |
| End Page: |
64 |