Kernel Metadata Declaration — on demand

What the DOI identifies

A Machine Learning Framework For Data Poisoning Attacks

10.46243/jst.2023.v8.i07.pp169-176 · JournalArticle — an article in a journal · Digital · Visual · Language · en

Published 2023-08-07

Part of Journal of Science & Technology · ISSN 2456-5660 · vol. 8 · no. 7 · pp. 169–176

Principal agents

• Priyanka Narsingoju Priyanka Narsingoju (author → Author)
• Longman Publishers (publisher → Publisher)

Also in the record, outside the Kernel: the abstract, the licence, 4 links, 10 references. Source: Crossref (member 25296), registered 2024-02-16, last deposited 2026-09-17.

⬇ Record (JSON) ⬇ Declaration (XML)

System metadata — ISO 26324:2025, Annex B · DOI Handbook 10.1

Each element by the standard's name and the Handbook's (in grey), read off the record.

The record

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        "value": "Federated models are built by collecting model changes from participants. To maintain the secrecy of the training data, the aggregator has no visibility into how these updates are made by design.. This paper aims to explore the vulnerability of federated machine learning, focusing on attacking a federated multitasking learning framework. The framework enables resource-constrained node devices, such as mobile phones and IOT devices, to learn a shared model while keeping the training However, the communication protocol among attackers may take advantage of various nodes to conduct data poisoning assaults, which has been shown to pose a serious danger to the majority of machine learning models. The paper formulates the problem of computing optimal poisoning attacks on federated multitask learning as a bi-level program that is adaptive to arbitrary choice of target nodes and source attacking nodes.The authors propose a novel systems-aware optimization method, Attack confederated Learning(AT2FL), which is efficiency to derive the implicit gradients for poisoned data and further compute optimal attack strategies in the federated machine learning",
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            "doi": "10.1609/aaai.v30i1.10237",
            "unstructured": "Alfeld, S., Zhu, X., & Barford, P. (2016). Data Poisoning Attacks against Autoregressive Models. Proceedings of the AAAI Conference on Artificial Intelligence, 30(1). https://doi.org/10.1609/ aaai.v30i1.10237"
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            "doi": "10.48550/arxiv.1807.00459",
            "unstructured": "Vitaly Shmatikov, Yiqing Hua, Deborah Estrin, Eugene Bagdasaryan, and Andreas Veit. federated learning backdoor techniques. 2018. arXiv preprint arXiv:1807.00459. https://doi. org/10.48550/arXiv.1807.00459"
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            "doi": "10.1007/s10994-010-5188-5",
            "unstructured": "Barreno, M., Nelson, B., Joseph, A.D. et al. The security of machine learning. Mach Learn 81, 121–148(2010). https://doi.org/10.1007/ s10994-010-5188-5"
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            "key": "ref4",
            "doi": "10.1109/tpds.2022.3218649",
            "unstructured": "Zhang S,Wang C and Zomaya C. Robustness Analysis and Enhancement of Deep Reinforcement Learning-Based Schedulers. IEEE Transactions on Parallel and Distributed Systems. 10.1109/ TPDS.2022.3218649. 34:1. (346-357 https:// ieeexplore.ieee.org/document/9937194/"
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        {
            "key": "ref5",
            "doi": "10.48550/arxiv.1206.6389",
            "unstructured": "Pavel Laskov, Blaine Nelson, and Batista Biggio. assaults with poison on support vector machines. 2012’s arXiv preprint 1206.6389 https://doi. org/10.48550/arXiv.1206.6389"
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            "doi": "10.5244/c.28.6",
            "unstructured": "Andrew Zisserman, Karen Simonyan, Ken Chatfield, and Andrea Vedaldi. The devil is once again in the details: a thorough examination of convolution networks. 2014 arXiv preprint 1405.3531. https://doi.org/10.48550/ arXiv.1405.353"
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            "unstructured": "Massimo Fornasier, Ignace Loris, and Ingrid Daubechies. Accelerated projected gradient approach for sparsity-constrained linear inverse problems. 2008;14(5-6):764–792;Journal of Fourier Analysis and Applications. https://doi. org/10.48550/arXiv.0706.4297"
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            "doi": "10.1145/2046684.2046692",
            "unstructured": "Ling Huang, JD Tygar, Anthony D. Joseph, Blaine Nelson, and Benjamin I. P. Rubinstein. Machine learning that is hostile. Pages 43–58 in the book Proceedings of the Fourth ACM Workshop on Security and Artificial Intelligence. ACM, 2011. https://ieeexplore.ieee.org/ document/9887796/"
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