[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118309-en":3,"doc-seo-118309-105":29,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},118309,7971461740909,"Levi","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","A Machine Learning Framework For Data Poisoning Attacks - Journal Article","Federated models are trained by aggregating participant updates while the aggregator lacks visibility into how updates are generated, creating an inherent attack surface. This paper investigates vulnerabilities in federated machine learning by targeting a federated multitask learning framework with communication-driven data poisoning assaults. It formulates optimal poisoning as an adaptive bi-level program over arbitrary target and source attacking nodes. The proposed system-aware optimization method, AT2FL, derives implicit gradients for poisoned data and computes optimal attack strategies, followed by experiments validating effectiveness on real-world datasets.","Journal of Science and Technology  \nISSN: 2456-5660 Volume 8, Issue 07 (July-2023)  \n [www.jst.org.in](www.jst.org.in) DOI:[https://doi.org/10.46243/jst.2023.v8.i07.pp169-176](https://doi.org/10.46243/jst.2023.v8.i07.pp169-176)   \nA Machine Learning Framework For Data Poisoning Attacks  \nPriyanka Narsingoju1 | Dr.D.Srinivas Reddy2 |Dr.V.Bapuji3  \n1Department of MCA,Vaageswari College Of Engineering, Karimnagar.  \n2Associate Professor,Department of MCA,Vaageswari College Of Engineering, Karimnagar.  \n3Professor & HoD, Department of MCA,Vaageswari College Of Engineering, Karimnagar.  \nTo Cite this Article  \nPriyanka Narsingoju, Dr.D.Srinivas Reddy,Dr.V.Bapuji ,“A Machine Learning Framework For Data  \nPoisoning Attacks ” Journal of Science and Technology, Vol. 08, Issue 07,- July 2023, pp169-176 Article Info  \nReceived: 06-06-2023 Revised: 06-07-2023 Accepted: 17-07-2023 Published: 27-07-2023  \nABSTRACT  \nFederated 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.  \nKEYWORDS: Federated machine learning, Vulnerability,Arbitrary, Attack on federated machine learning(AT2FL), Gradients.  \nINTRODUCTION  \nMachine learning has been widely applied in various applications, such as spam filtering and natural gas price prediction[1] . However, the reliability and security of these systems have been a concern, including adversaries. Researchers can rely on public crowd sourcing platforms or Private teams to collect training datasets, but both have the potential to be injected corrupted or poisoned data by attackers. It is crucial to research how well machine learning operates under poisoning it attempts in order to increase the resilience of real-world machine learning systems. Exploratory attacks and causal assaults are two categories of attack tactics. The n nodes in this federated learning system are shown by distinct colors. Corrupted or poisoned data is injected into certain nodes, whereas clean data is the sole data present in other nodes. The fundamental idea behind federated machine learning is to develop machine learning models based on data sets dispersed across numerous devices, while limiting data loss.  \nPublished by: Longman Publishers [www.jst.org.in](www.jst.org.in)  \nPage | 169  \nJournal of Science and Technology  \nISSN: 2456-5660 Volume 8, Issue 07 (July-2023)  \n[www.jst.org.in](www.jst.org.in) DOI:[https://doi.org/10.46243/jst.2023.v8.i07.pp169-176](https://doi.org/10.46243/jst.2023.v8.i07.pp169-176)  \nFig.1 illustrates our data poisoning attack model for federated machine learning.  \nAlthough recent advancements have focused on overcoming statistical challenges (i.e. , data collected across the network is in a nonrigid manner, with data on each node generated by a distinct distribution) or improving privacy preservation, attempts to make federated learning more reliable under poisoning attacks are still scarce. Consider multiple distinct e-com","cbCaimMDfKi0eDVy","https://ap.wps.com/l/cbCaimMDfKi0eDVy","pdf",378368,1,"English","en",105,"# Abstract\n# Introduction\n## Federated learning under poisoning\n## Attack model and optimization formulation\n## Proposed method and experimental validation","[{\"question\":\"What vulnerability does the paper focus on in federated learning?\",\"answer\":\"The paper studies how federated machine learning can be attacked through data poisoning, particularly against a federated multitask learning framework.\"},{\"question\":\"How is the optimal poisoning attack problem formulated?\",\"answer\":\"The study formulates computing optimal poisoning attacks as a bi-level program that adapts to arbitrary choices of target nodes and source attacking nodes.\"},{\"question\":\"What is AT2FL and what does it do?\",\"answer\":\"AT2FL is a systems-aware optimization approach that efficiently derives implicit gradients for poisoned data and then computes optimal attack strategies for federated machine learning.\"}]","A Machine Learning Framework For Data Poisoning Attacks - 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