[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122624-en":3,"doc-seo-122624-105":30,"detail-sidebar-cat-0-en-105":92},{"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":20,"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},122624,1099513958762,"Logic","https://ap-avatar.wpscdn.com/avatar/1000023916a998db790?x-image-process=image/resize,m_fixed,w_180,h_180&k=1784791008015729253",8,"Research & Report","Efficient Secure Aggregation for Privacy-Preserving Federated Machine Learning - E-seaML Secure Aggregation Protocol","Federated learning enables training machine learning models on distributed data without exposing users’ raw information by sending the model to clients, computing local updates, and aggregating them at a central server. Secure aggregation protects against data leakage from local updates, yet many existing protocols impose heavy communication and computation costs and do not efficiently handle large model update vectors. This paper proposes E-seaML, a one-round secure aggregation protocol that substantially reduces overhead for both users and the server while supporting integrity verification of the final model via a proof of honest aggregation.","Efficient Secure Aggregation for Privacy-Preserving Federated Machine Learning  \nRouzbeh Behnia University of South Florida  \nMohammadreza Ebrahimi University of South Florida  \nAramn Riasi Virginia Tech  \nBalaji Padmanabhan University of South Florida  \nThang Hoang Virginia Tech  \narXiv :2304 .0384 1v 1 [ cs .CR] 7 Apr 2023  \nAbstract  \nFederated learning introduces a novel approach to training machine learning (ML) models on distributed data while preserving user's data privacy. This is done by distributing the model to clients to perform training on their local data and computing the final model at a central server. To prevent any data leakage from the local model updates, various works with focus on secure aggregation for privacy preserving federated learning have been proposed. Despite their merits, most of the existing protocols still incur high communication and computation overhead on the participating entities and might not be optimized to efficiently handle the large update vectors for ML models.  \nIn this paper, we present E-seaML, a novel secure aggregation protocol with high communication and computation efficiency. E-seaML only requires one round of communication in the aggregation phase and it is up to 318 􀀂 and 1224􀀂 faster for the user and the server (respectively) as compared to its most efficient counterpart. E-seaML also allows for efficiently verifying the integrity of the final model by allowing the aggregation server to generate a proof of honest aggregation for the participating users. This high efficiency and versatility is achieved by extending (and weakening) the assumption of the existing works on the set of honest parties (i.e., users) to a set of assisting nodes. Therefore, we assume a set of assisting nodes which assist the aggregation server in the aggregation process. We also discuss, given the minimal computation and communication overhead on the assisting nodes, how one could assume a set of rotating users to as assisting nodes in each iteration. We provide the open-sourced implementation of E-seaML for public verifiability and testing.  \n1 Introduction  \nMachine Learning (ML) has provided breakthrough results that outperform humans in many critical fields such as medical screening [1], cyber threat hunting [2], object recognition [3],  \nand sequential decision making [4] . Today, many companies are eager to benefit from ML on their private data but they cannot afford the infrastructure needed to run large ML modelson-premise. This has resulted in a surge in collaborative learning, in which several parties join forces to contribute to a single learning problem of interest. The widespread adoption of cloud-based services and mobile devices has enabled the generation of a huge amount of rich data and an untapped source of computation empowered by commodity hardware [5] . In such a setting, collaborative learning has provided the opportunity to leverage the fragmented data residing locally at other parties to increase the ML model's accuracy.  \nHowever, such data often include sensitive private information which mandates severe restrictions on data transmission to other locations for training (e.g., due to compliance related restrictions such as HIPPA or GDPR [6]) . Accordingly, collecting the data from different isolated islands across different organizations [5] or large set of distributed users [7, 8] while assuming a central trusted party is not feasible. As a result, companies who govern sensitive private data (e.g., electronic health records or personal identifiable information) have been fighting an uphill battle in which they frequently have to choose between gaining collaborative ML capabilities and giving up on their data privacy.  \nTo address this challenge, Federated Learning (FL) has emerged as a promising solution to train a centralized ML models by relying on the contribution of multiple parties while maintaining the privacy of the local data [9, 10] . In an FL setting, a central server is tas","cbCaiaYuG9cNi9ZC","https://ap.wps.com/l/cbCaiaYuG9cNi9ZC","pdf",544796,1,18,"English","en",105,"# Introduction\n## Federated learning overview\n## Privacy risks and secure aggregation\n## Existing approaches and their limitations","[{\"question\":\"What problem does federated learning address regarding data privacy?\",\"answer\":\"Federated learning trains models using distributed data by keeping local data confidential while only transmitting model updates for server-side aggregation. This avoids direct data movement to other locations for training.\"},{\"question\":\"Why do secure aggregation protocols matter in federated learning?\",\"answer\":\"Local model updates can leak sensitive information about users’ data. Secure aggregation computes the aggregated result without revealing individual updates, reducing the risk of information leakage.\"},{\"question\":\"What efficiency improvement does E-seaML provide over prior secure aggregation protocols?\",\"answer\":\"E-seaML performs aggregation with only one communication round and achieves significant reductions in communication and computation overhead for users and the server compared with the most efficient existing counterpart.\"}]","Efficient Secure Aggregation for Privacy-Preserving Federated Machine Learning - E-seaML Secure Aggregation Protocol | PDF",1785811782,45,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"efficient-secure-aggregation-for-privacy-preserving-federated-machine-learning-e-seaml-secure-aggregation-protocol","",{"@graph":36,"@context":86},[37,54,69],{"@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/efficient-secure-aggregation-for-privacy-preserving-federated-machine-learning-e-seaml-secure-aggregation-protocol/122624/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05","2026-08-04",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What problem does federated learning address regarding data privacy?","Question",{"text":76,"@type":77},"Federated learning trains models using distributed data by keeping local data confidential while only transmitting model updates for server-side aggregation. This avoids direct data movement to other locations for training.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Why do secure aggregation protocols matter in federated learning?",{"text":81,"@type":77},"Local model updates can leak sensitive information about users’ data. Secure aggregation computes the aggregated result without revealing individual updates, reducing the risk of information leakage.",{"name":83,"@type":74,"acceptedAnswer":84},"What efficiency improvement does E-seaML provide over prior secure aggregation protocols?",{"text":85,"@type":77},"E-seaML performs aggregation with only one communication round and achieves significant reductions in communication and computation overhead for users and the server compared with the most efficient existing counterpart.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":46,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]