Aggregation algorithm based on consensus verification
Distributed learning, as the most popular solution for training large-scale data for deep learning, consists of multiple participants collaborating on data training tasks. However, the malicious behavior of some during the training process, like Byzantine participants who would interrupt or control...
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| Vydáno v: | Scientific reports Ročník 13; číslo 1; s. 12923 - 14 |
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| Jazyk: | angličtina |
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09.08.2023
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| ISSN: | 2045-2322, 2045-2322 |
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| Abstract | Distributed learning, as the most popular solution for training large-scale data for deep learning, consists of multiple participants collaborating on data training tasks. However, the malicious behavior of some during the training process, like Byzantine participants who would interrupt or control the learning process, will trigger the crisis of data security. Although recent existing defense mechanisms use the variability of Byzantine node gradients to clear Byzantine values, it is still unable to identify and then clear the delicate disturbance/attack. To address this critical issue, we propose an algorithm named consensus aggregation in this paper. This algorithm allows computational nodes to use the information of verification nodes to verify the effectiveness of the gradient in the perturbation attack, reaching a consensus based on the effective verification of the gradient. Then the server node uses the gradient as the valid gradient for gradient aggregation calculation through the consensus reached by other computing nodes. On the MNIST and CIFAR10 datasets, when faced with Drift attacks, the proposed algorithm outperforms common existing aggregation algorithms (Krum, Trimmed Mean, Bulyan), with accuracies of 93.3%, 94.06% (MNIST dataset), and 48.66%, 51.55% (CIFAR10 dataset), respectively. This is an improvement of 3.0%, 3.8% (MNIST dataset), and 19.0%, 26.1% (CIFAR10 dataset) over the current state-of-the-art methods, and successfully defended against other attack methods. |
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| AbstractList | Distributed learning, as the most popular solution for training large-scale data for deep learning, consists of multiple participants collaborating on data training tasks. However, the malicious behavior of some during the training process, like Byzantine participants who would interrupt or control the learning process, will trigger the crisis of data security. Although recent existing defense mechanisms use the variability of Byzantine node gradients to clear Byzantine values, it is still unable to identify and then clear the delicate disturbance/attack. To address this critical issue, we propose an algorithm named consensus aggregation in this paper. This algorithm allows computational nodes to use the information of verification nodes to verify the effectiveness of the gradient in the perturbation attack, reaching a consensus based on the effective verification of the gradient. Then the server node uses the gradient as the valid gradient for gradient aggregation calculation through the consensus reached by other computing nodes. On the MNIST and CIFAR10 datasets, when faced with Drift attacks, the proposed algorithm outperforms common existing aggregation algorithms (Krum, Trimmed Mean, Bulyan), with accuracies of 93.3%, 94.06% (MNIST dataset), and 48.66%, 51.55% (CIFAR10 dataset), respectively. This is an improvement of 3.0%, 3.8% (MNIST dataset), and 19.0%, 26.1% (CIFAR10 dataset) over the current state-of-the-art methods, and successfully defended against other attack methods. Distributed learning, as the most popular solution for training large-scale data for deep learning, consists of multiple participants collaborating on data training tasks. However, the malicious behavior of some during the training process, like Byzantine participants who would interrupt or control the learning process, will trigger the crisis of data security. Although recent existing defense mechanisms use the variability of Byzantine node gradients to clear Byzantine values, it is still unable to identify and then clear the delicate disturbance/attack. To address this critical issue, we propose an algorithm named consensus aggregation in this paper. This algorithm allows computational nodes to use the information of verification nodes to verify the effectiveness of the gradient in the perturbation attack, reaching a consensus based on the effective verification of the gradient. Then the server node uses the gradient as the valid gradient for gradient aggregation calculation through the consensus reached by other computing nodes. On the MNIST and CIFAR10 datasets, when faced with Drift attacks, the proposed algorithm outperforms common existing aggregation algorithms (Krum, Trimmed Mean, Bulyan), with accuracies of 93.3%, 94.06% (MNIST dataset), and 48.66%, 51.55% (CIFAR10 dataset), respectively. This is an improvement of 3.0%, 3.8% (MNIST dataset), and 19.0%, 26.1% (CIFAR10 dataset) over the current state-of-the-art methods, and successfully defended against other attack methods.Distributed learning, as the most popular solution for training large-scale data for deep learning, consists of multiple participants collaborating on data training tasks. However, the malicious behavior of some during the training process, like Byzantine participants who would interrupt or control the learning process, will trigger the crisis of data security. Although recent existing defense mechanisms use the variability of Byzantine node gradients to clear Byzantine values, it is still unable to identify and then clear the delicate disturbance/attack. To address this critical issue, we propose an algorithm named consensus aggregation in this paper. This algorithm allows computational nodes to use the information of verification nodes to verify the effectiveness of the gradient in the perturbation attack, reaching a consensus based on the effective verification of the gradient. Then the server node uses the gradient as the valid gradient for gradient aggregation calculation through the consensus reached by other computing nodes. On the MNIST and CIFAR10 datasets, when faced with Drift attacks, the proposed algorithm outperforms common existing aggregation algorithms (Krum, Trimmed Mean, Bulyan), with accuracies of 93.3%, 94.06% (MNIST dataset), and 48.66%, 51.55% (CIFAR10 dataset), respectively. This is an improvement of 3.0%, 3.8% (MNIST dataset), and 19.0%, 26.1% (CIFAR10 dataset) over the current state-of-the-art methods, and successfully defended against other attack methods. Abstract Distributed learning, as the most popular solution for training large-scale data for deep learning, consists of multiple participants collaborating on data training tasks. However, the malicious behavior of some during the training process, like Byzantine participants who would interrupt or control the learning process, will trigger the crisis of data security. Although recent existing defense mechanisms use the variability of Byzantine node gradients to clear Byzantine values, it is still unable to identify and then clear the delicate disturbance/attack. To address this critical issue, we propose an algorithm named consensus aggregation in this paper. This algorithm allows computational nodes to use the information of verification nodes to verify the effectiveness of the gradient in the perturbation attack, reaching a consensus based on the effective verification of the gradient. Then the server node uses the gradient as the valid gradient for gradient aggregation calculation through the consensus reached by other computing nodes. On the MNIST and CIFAR10 datasets, when faced with Drift attacks, the proposed algorithm outperforms common existing aggregation algorithms (Krum, Trimmed Mean, Bulyan), with accuracies of 93.3%, 94.06% (MNIST dataset), and 48.66%, 51.55% (CIFAR10 dataset), respectively. This is an improvement of 3.0%, 3.8% (MNIST dataset), and 19.0%, 26.1% (CIFAR10 dataset) over the current state-of-the-art methods, and successfully defended against other attack methods. |
| ArticleNumber | 12923 |
| Author | Jiwei, Qin Shichao, Li |
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| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/37558756$$D View this record in MEDLINE/PubMed |
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| References_xml | – reference: Castro, M., & Liskov, B. Practical byzantine fault tolerance. ACM Trans. Comput. Syst. (2002). – reference: Mhamdi, E., Guerraoui, R. & Rouault, S. The hidden vulnerability of distributed learning in byzantium (2018). – reference: Nakamoto, S. Bitcoin: A peer-to-peer electronic cash system (2008). – reference: Ming, Y., Xuexian, H., Qihui, Z., Jianghong, W., & Wenfen, L. Federated learning scheme for mobile network based on reputation evaluation mechanism and blockchain. Chin. J. Netw. Inf. Secur.7 (2021). – reference: Rakin, A. S., He, Z. & Fan, D. Tbt: Targeted neural network attack with bit trojan. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 13198–13207 (2020). – reference: LiMAndersenDGSmolaAYuKCommunication efficient distributed machine learning with the parameter serverAdv. Neural Inf. Process. Syst.201411927 – reference: Duchi, J. C., Agarwal, A. & Wainwright, M. J. Distributed dual averaging in networks. In Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010. Proceedings of a meeting held 6–9 December 2010, Vancouver, British Columbia, Canada (2010). – reference: RobbinsHMonroSA stochastic approximation methodAnn. Math. Stat.1951224004074266810.1214/aoms/11777295860054.05901 – reference: Baruch, M., Baruch, G., & Goldberg, Y. Circumventing defenses for distributed learning: A little is enough, 2019. – reference: Bagdasaryan, E., Veit, A., Hua, Y., Estrin, D. & Shmatikov, V. How to backdoor federated learning (2018). – reference: Dean, J. Mapreduce : Simplified data processing on large clusters. In Symposium on Operating System Design & Implementation (2004). – reference: Shen, S., Tople, S. & Saxena, P. Auror: Defending against poisoning attacks in collaborative deep learning systems. In Conference on Computer Security Applications (2016). – reference: Dumford, J. & Scheirer, W. Backdooring convolutional neural networks via targeted weight perturbations. In 2020 IEEE International Joint Conference on Biometrics (IJCB), 1–9 (IEEE, 2020). – reference: Blanchard, P., Mhamdi, E., Guerraoui, R. & Stainer, J. Machine learning with adversaries: Byzantine tolerant gradient descent. In Neural Information Processing Systems (2017). – reference: Coates, A., et al. Large scale distributed deep networks (2011). – reference: Li, M., Andersen, D. G., Park, J. W., Smola, A. J. & Su, B. Y. Scaling distributed machine learning with the parameter server. 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| Title | Aggregation algorithm based on consensus verification |
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