[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120819-en":3,"doc-seo-120819-105":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},120819,16904993612988,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",6,"Technology","A Design Toolbox for the Development of Collaborative Distributed Machine Learning Systems","Collaborative distributed machine learning (CDML) enables training ML models using data from multiple parties while preserving confidentiality. The document addresses a key development challenge: numerous CDML system designs exist—such as assisted learning, federated learning, and split learning—with differing traits like agent autonomy, model confidentiality, and fault tolerance. It is difficult for developers to select and tailor designs to match specific use-case requirements, and unsuitable choices can undermine intended purposes. To solve this, the authors propose a CDML design toolbox and derive system archetypes that capture distinct trait combinations aligned to requirements.","A Design Toolbox for the Development of Collaborative Distributed Machine Learning  \nSystems  \nDavid Jin∗ , Niclas Kannengießer∗†, Sascha Rank∗†, Ali Sunyaev∗†  \n∗ Karlsruhe Institute of Technology, Germany  \n†KASTEL Security Research Labs, Germany  \n{david.jin, niclas.kannengiesser, sascha.rank, [sunyaev](sunyaev}@kit.edu)[}](sunyaev}@kit.edu)[@kit.edu](sunyaev}@kit.edu)  \narXiv :2309 . 16584v1 [ cs .MA] 28 Sep 2023  \nAbstract—To leverage training data for the sufficient training of ML models from multiple parties in a confidentialitypreserving way, various collaborative distributed machine learning (CDML) system designs have been developed, for example, to perform assisted learning, federated learning, and split learning. CDML system designs show different traits, for example, high agent autonomy, machine learning (ML) model confidentiality, and fault tolerance. Facing a wide variety of CDML system designs with different traits, it is difficult for developers to design CDML systems with traits that match use case requirements ina targeted way. However, inappropriate CDML system designs may result in CDML systems failing their envisioned purposes. We developed a CDML design toolbox that can guide the development of CDML systems. Based on the CDML design toolbox, we present CDML system archetypes with distinct key traits that can support the design of CDML systems to meet use case requirements.  \nIndex Terms—collaborative distributed machine learning (CDML), privacy-enhancing technologies (PETs), assisted learning, federated learning (FL), split learning, swarm learning, multi-agent systems (MAS).  \nI. INTRODUCTION  \nThe training of machine learning (ML) models requires sufficient training data in terms of quantity and quality to make meaningful predictions with little generalization error. Sufficient training data is, however, seldom available from a single party (e.g., a bank or a hospital), which can prevent the adequate training of ML models [1] . Inadequate training of ML models can result in large generalization errors, rendering ML models ineffective [2] .  \nTo reduce generalization errors of ML models, developers request training data from multiple third parties. Training data retrievals from third parties are often subject to compliance, social, and technical challenges [3]–[5] that hinder the acquisition of sufficient training data. For example, strict data protection laws and regulations prohibit the disclosure of specific kinds of data, such as personal data by the General Data Protection Regulation of the European Union [6] and organizational data by the Healthcare Insurance Portability and Accountability Act of the USA [7] . From a social perspective, privacy behaviors of individuals restrict information flows to third parties based on personal preferences [8], preventing access to their training data. Insufficient computing resources inhibit the transfer of large data sets from data centers to  \ndevelopers in an acceptable time [3], [4] . To reduce generalization errors of ML models by using training data from multiple parties, an ML paradigm is required that solves those challenges.  \nCollaborative distributed ML (CDML) is an ML paradigm that can be implemented to overcome, in particular, compliance and technical challenges in using data from multiple parties to train ML models [9]–[14] . In CDML systems, such as federated learning systems [10], split learning systems [11], and swarm learning systems [14], each party operates at least one quasi-autonomous agent (referred to as agent in the following) . Agents in CDML systems train (parts of) ML modelson their local training data and self-controlled compute in a distributed manner. Agents only share their locally computed training results (interim results) with other agents, for example, gradients [15], activations [11], and (pseudo-)residuals [12] . Reconstructing training data from interim results is commonly difficult [9] . Using interim results received from","cbCaibacTBQFNy6E","https://ap.wps.com/l/cbCaibacTBQFNy6E","pdf",474236,1,21,"English","en",105,"# Introduction\n## Background and Challenges in Training ML with Distributed Data\n## CDML Paradigm and System Mechanisms\n## Motivation: Use Cases and Requirement Diversity","[{\"question\":\"Why is collaborative distributed machine learning (CDML) needed?\",\"answer\":\"Single parties rarely provide enough high-quality training data due to compliance, social, and technical constraints. CDML coordinates training across parties while keeping data confidentiality and improving practicality.\"},{\"question\":\"What traits distinguish CDML system designs?\",\"answer\":\"CDML designs vary in agent autonomy, how ML model confidentiality is handled, and fault tolerance. These differences influence whether a design fits a particular use case.\"},{\"question\":\"How does the proposed CDML design toolbox help developers?\",\"answer\":\"The toolbox guides CDML system development by organizing distinct design choices and supporting the creation of CDML archetypes with key traits matched to use-case requirements.\"}]","A Design Toolbox for the Development of Collaborative Distributed Machine Learning Systems | PDF",1785732174,53,{"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},"a-design-toolbox-for-the-development-of-collaborative-distributed-machine-learning-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/technology/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/a-design-toolbox-for-the-development-of-collaborative-distributed-machine-learning-systems/120819/",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","2026-08-03",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},"Why is collaborative distributed machine learning (CDML) needed?","Question",{"text":75,"@type":76},"Single parties rarely provide enough high-quality training data due to compliance, social, and technical constraints. CDML coordinates training across parties while keeping data confidentiality and improving practicality.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What traits distinguish CDML system designs?",{"text":80,"@type":76},"CDML designs vary in agent autonomy, how ML model confidentiality is handled, and fault tolerance. These differences influence whether a design fits a particular use case.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the proposed CDML design toolbox help developers?",{"text":84,"@type":76},"The toolbox guides CDML system development by organizing distinct design choices and supporting the creation of CDML archetypes with key traits matched to use-case requirements.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,113,118,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":111,"slug":112},50,"technology",{"id":114,"doc_module":4,"doc_module_name":46,"category_name":115,"show_sort_weight":116,"slug":117},7,"Healthcare",40,"healthcare",{"id":119,"doc_module":4,"doc_module_name":46,"category_name":120,"show_sort_weight":121,"slug":122},8,"Research & Report",30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]