[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119057-en":3,"doc-seo-119057-105":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},119057,4398048950312,"Violet","https://ap-avatar.wpscdn.com/avatar/400002538284de19e3c?_k=1778320343897328908",8,"Research & Report","Unlocking the Potential of Collaborative AI - On the Socio-Technical Challenges of Federated Machine Learning","AI systems derive disruptive potential from big data, yet much valuable information remains scattered in data silos and inaccessible, leaving economic value unrealized. Federated Machine Learning offers a paradigm for creating models from decentralized, potentially siloed data, and can therefore unlock that value. Realizing this requires collaboration among data-owning parties and involves complex collaborative business models. This research uses systematic literature review, focus groups, and expert interviews to compile socio-technical challenges and extend the Business Model Canvas for early viability assessment.","Association for Information Systems  \nAIS Electronic Library (AISeL)  \n\n| ECIS 2023 Research Papers | ECIS 2023 Proceedings |\n| --- | --- |\n| 5-11-2023\u003Cbr>Unlocking the Potential of Collaborative AI -On the Socio-Technical Challenges of Federated Machine Learning\u003Cbr>Tobias Müller\u003Cbr>Technical University of Munich, [tobias1.mueller@tum.de](tobias1.mueller@tum.de)\u003Cbr>Milena Zahn\u003Cbr>Technical University of Munich, [milena.zahn@tum.de](milena.zahn@tum.de)\u003Cbr>Florian Matthes\u003Cbr>Technical University of Munich, [matthes@tum.de](matthes@tum.de)\u003Cbr>Follow this and additional works at: [https://aisel.aisnet.org/ecis2023_rp](https://aisel.aisnet.org/ecis2023_rp) |  |\n\nRecommended Citation  \nMüller, Tobias; Zahn, Milena; and Matthes, Florian, \"Unlocking the Potential of Collaborative AI-On the Socio-Technical Challenges of Federated Machine Learning\" (2023) . ECIS 2023 Research Papers. 245.  \n[https://aisel.aisnet.org/ecis2023_rp/245](https://aisel.aisnet.org/ecis2023_rp/245)  \nThis material is brought to you by the ECIS 2023 Proceedings at AIS Electronic Library (AISeL) . It has been accepted for inclusion in ECIS 2023 Research Papers by an authorized administrator of AIS Electronic Library (AISeL) . For more information, please [contact elibrary@aisnet.org](contact elibrary@aisnet.org).  \nUNLOCKING THE POTENTIAL OF COLLABORATIVE AI – ON THE SOCIO-TECHNICAL CHALLENGES OF FEDERATED MACHINE LEARNING  \nResearch Paper  \nTobias Müller, Technical University of Munich, School of Computation, Information and Technology, Department of Computer Science, Germany and SAP SE, Germany, [tobias1.mueller@tum.de](tobias1.mueller@tum.de)  \nMilena Zahn, Technical University of Munich, School of Computation, Information and Technology, Department of Computer Science, Germany and SAP SE, Germany, [milena.zahn@tum.de](milena.zahn@tum.de)  \nFlorian Matthes, Technical University of Munich, School of Computation, Information and Technology, Department of Computer Science, Germany, [matthes@tum.de](matthes@tum.de)  \nAbstract  \nThe disruptive potential of AI systems roots in the emergence of big data. Yet, a significant portion is scattered and locked in data silos, leaving its potential untapped. Federated Machine Learning is a novel AI paradigm enabling the creation of AI models from decentralized, potentially siloed data. Hence, Federated Machine Learning could technically open data silos and therefore unlock economic potential. However, this requires collaboration between multiple parties owning data silos. Setting up collaborative business models is complex and often a reason for failure. Current literature lacks guidelines on which aspects must be considered to successfully realize collaborative AI projects. This research investigates the challenges of prevailing collaborative business models and distinct aspects of Federated Machine Learning. Through a systematic literature review, focus group, and expert interviews, we provide a systemized collection of socio-technical challenges and an extended Business Model Canvas for the initial viability assessment of collaborative AI projects.  \nKeywords: Federated Machine Learning, Collaborative Data Processing, Business Model, Alliances  \n1 Introduction  \nArtificial Intelligence (AI) had an immense economic impact in the last couple of years. In 2021 alone, the market ofAI-based services including software, hardware and services exceeded 500$ billion with a five-year compound annual growth rate of 17.5%(Forradellas and Gallastegui, 2021) . The potential profitability raise is currently estimated by an average of 38%, which implies an economic impact of $14 trillion until 20351 . Unmistakably, the usage of AI enables new, unprecedented business models with a monumental impact on the industry. The main enabler for this disruptive new market is the emergence of big data, which forms the fundamental basis for AI systems. Even though vast amounts of data is freely available, a considerable amount of the world’s data is ","cbCaiufiQCJfsufo","https://ap.wps.com/l/cbCaiufiQCJfsufo","pdf",545075,1,15,"English","en",105,"# Introduction\n## Big data, AI impact, and data silos\n## Federated Machine Learning as an enabling paradigm\n## Socio-technical challenges and collaboration needs\n## Research approach and contributions","[{\"question\":\"Why is data silos a problem for realizing AI value?\",\"answer\":\"A significant portion of data remains decentralized and locked in silos, limiting access and preventing the full exploitation of generated economic potential for AI-driven business models.\"},{\"question\":\"How does Federated Machine Learning address privacy and data silos?\",\"answer\":\"FedML trains a global model and distributes it to participants, then performs local training and returns updates such as gradients, keeping data on the client device to avoid privacy leakage.\"},{\"question\":\"What does the research contribute to collaborative AI projects?\",\"answer\":\"Through systematic literature review, focus group, and expert interviews, it provides a systemized collection of socio-technical challenges and an extended Business Model Canvas to support initial viability assessment of collaborative AI projects.\"}]","Unlocking the Potential of Collaborative AI - On the Socio-Technical Challenges of Federated Machine Learning | PDF",1785722121,38,{"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},"unlocking-the-potential-of-collaborative-ai-on-the-socio-technical-challenges-of-federated-machine-learning","",{"@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/unlocking-the-potential-of-collaborative-ai-on-the-socio-technical-challenges-of-federated-machine-learning/119057/",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","2026-08-03",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},"Why is data silos a problem for realizing AI value?","Question",{"text":76,"@type":77},"A significant portion of data remains decentralized and locked in silos, limiting access and preventing the full exploitation of generated economic potential for AI-driven business models.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does Federated Machine Learning address privacy and data silos?",{"text":81,"@type":77},"FedML trains a global model and distributes it to participants, then performs local training and returns updates such as gradients, keeping data on the client device to avoid privacy leakage.",{"name":83,"@type":74,"acceptedAnswer":84},"What does the research contribute to collaborative AI projects?",{"text":85,"@type":77},"Through systematic literature review, focus group, and expert interviews, it provides a systemized collection of socio-technical challenges and an extended Business Model Canvas to support initial viability assessment of collaborative AI projects.","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"]