[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118415-en":3,"doc-seo-118415-105":30,"detail-sidebar-cat-0-en-105":91},{"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":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118415,1099514067438,"River Wang","https://ap-avatar.wpscdn.com/avatar/100002539ee87300030?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780474512215547542",8,"Research & Report","Advancing Secure and Efficient Distributed Machine Learning for Edge Systems","This dissertation advances secure and efficient distributed machine learning for edge computing environments. It addresses challenges of deploying hybrid edge-cloud ML systems while meeting privacy and data protection requirements, including threat models and regulatory constraints. The work introduces Croesus, a multi-stage processing and transaction framework for video analytics that balances accuracy and performance. It also presents HUSH, a shared-layer approach for sharding-based machine unlearning with leakage analysis and differentially private mechanisms.","UC Santa Cruz  \nUC Santa Cruz Electronic Theses and Dissertations  \nTitle  \nAdvancing Secure and Efficient Distributed Machine Learning for Edge Systems  \nPermalink  \n[https://escholarship.org/uc/item/6468c70q](https://escholarship.org/uc/item/6468c70q)  \nAuthor  \nGazzaz, Samaa  \nPublication Date  \n2024  \nPeer reviewed|Thesis/dissertation  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nUNIVERSITY OF CALIFORNIA  \nSANTA CRUZ  \nADVANCING SECURE AND EFFICIENT DISTRIBUTED MACHINE LEARNING FOR EDGE SYSTEMS  \nA dissertation submitted in partial satisfaction of the requirements for the degree of  \nDOCTOR OF PHILOSOPHY  \nin  \nCOMPUTER SCIENCE AND ENGINEERING  \nby  \nSamaa Tariq Gazzaz  \nDecember 2024  \nThe Dissertation of Samaa Tariq Gazzaz is approved:  \n\n| Katia Obraczka, Chair |\n| --- |\n| Faisal Nawab |\n\nAlvaro Cardenas  \nPeter Biehl  \nVice Provost and Dean of Graduate Studies  \nCopyright © by Samaa Tariq Gazzaz  \n2024  \nContents  \nFigures and Tables v  \nAbstract vii  \nDedication ix  \nAcknowledgments x  \n1 Introduction 1  \n1. 1 Introducion and Motivation . . . . . . . . . . . . . . . . . . . . . . . . 1  \n1.2 Research Gaps and Objectives . . . . . . . . . . . . . . . . . . . . . . 4  \n1.3 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5  \n2 Background 8  \n2.1 Edge Computing and Hybrid Edge-Cloud Systems ............ 8  \n2.1.1 Challenges in Edge-Cloud ML Systems ............. 9  \n2.2 Distributed Machine Learning ...................... 10  \n2.3 Privacy and Data Protection Regulations ................. 12  \n2.3.1 Overview of Data Privacy Regulations .............. 13  \n2.3.2 Privacy Attacks .......................... 13  \n2.4 Machine Unlearning ........................... 14  \n2.4.1 Overview of Machine Unlearning ................ 14  \n2.4.2 Privacy-Preserving Approaches ................. 15  \n2.4.3 Sharding-Based Machine Unlearning .............. 15  \n3 Croesus: Multi-Stage Processing and Transactions for Video-Analyticsin Edge-Cloud Systems 17  \n3.1 System and Programming Model ..................... 20  \n3.2 Accuracy-Performance Trade-off in Object Detection .......... 24  \n3.3 Croesus Design .............................. 25  \n3.3. 1 Initial-Final subsection Interaction . . . . . . . . . . . . . . . . 26  \n3.3.2 Algorithms ............................ 27  \n3.3.3 Bandwidth Thresholding ..................... 31  \nContents  \n3.3.4 Generalizing Multi-Stage Processing ............... 34  \n3.4 Multi-Stage Transactions ......................... 35  \n3.4.1 Multi-Stage Transaction Model ................. 35  \n3.4.2 Safety ............................... 35  \n3.4.3 Multi-Stage Serializability (MS-SR) ............... 37  \n3.4.4 Multi-Stage Invariant Confluence with Apologies (MS-IA) ... 41  \n3.4.5 Multi-Partition Operations .................... 46  \n3.5 Croesus Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47  \n3.5.1 Experimental setup ........................ 47  \n3.5.2 Experimental results ....................... 52  \n3.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61  \n4 HUSH: The Case for Shared LayersinSharding-Based Machine Unlearning 62  \n4. 1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66  \n4.2 HUSH Design ............................... 71  \n4.2.1 System Model .......................... 71  \n4.2.2 Model Architecture Overview . . . . . . . . . . . . . . . . . . 74  \n4.2.3 Detailed Design .......................... 75  \n4.3 information leakage analysis ....................... 79  \n4.3.1 Information Leakage Overview ................. 79  \n4.3.2 Experimental Verification using MIA .............. 81  \n4.3.3 HUSH-DP: Differentially-Private Trunk ............. 87  \n4.4 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90  \n4.4.1 Experimental Setup . . . . . . . . . . . . . . . . . . . . . . . . 90  \n4.4.2 Baselines and metrics . . . . . . . . . .","cbCaidNfoUnX3Xco","https://ap.wps.com/l/cbCaidNfoUnX3Xco","pdf",2838321,1,128,"English","en",105,"# Introduction\n## Research Gaps and Objectives\n## Contributions\n# Background\n## Edge Computing and Hybrid Edge-Cloud Systems\n## Distributed Machine Learning\n## Privacy and Data Protection Regulations\n## Machine Unlearning\n# Croesus: Multi-Stage Processing and Transactions for Video-Analytics in Edge-Cloud Systems\n## System and Programming Model\n## Accuracy-Performance Trade-off in Object Detection\n## Croesus Design\n## Multi-Stage Transactions\n## Croesus Evaluation\n## Conclusion\n# HUSH: The Case for Shared Layers in Sharding-Based Machine Unlearning\n## Overview\n## HUSH Design\n## Information Leakage Analysis\n## Evaluation\n## Related Work\n# Conclusions and Future Directions","[{\"question\":\"What problem does the dissertation target in edge-based distributed machine learning?\",\"answer\":\"It targets secure and efficient distributed machine learning deployment in edge systems, where hybrid edge-cloud coordination and privacy protection are major constraints.\"},{\"question\":\"What is Croesus and what does it optimize?\",\"answer\":\"Croesus is a multi-stage processing and transaction framework for video analytics in edge-cloud systems. It focuses on balancing accuracy and performance while enabling safe multi-stage behavior.\"},{\"question\":\"How does the dissertation handle machine unlearning securely?\",\"answer\":\"It introduces HUSH for sharding-based machine unlearning using shared layers, including an information leakage analysis and a differentially private design to reduce privacy risk.\"}]","Advancing Secure and Efficient Distributed Machine Learning for Edge Systems | PDF",1785683508,323,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"advancing-secure-and-efficient-distributed-machine-learning-for-edge-systems","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/advancing-secure-and-efficient-distributed-machine-learning-for-edge-systems/118415/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the dissertation target in edge-based distributed machine learning?","Question",{"text":75,"@type":76},"It targets secure and efficient distributed machine learning deployment in edge systems, where hybrid edge-cloud coordination and privacy protection are major constraints.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What is Croesus and what does it optimize?",{"text":80,"@type":76},"Croesus is a multi-stage processing and transaction framework for video analytics in edge-cloud systems. 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