[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118301-en":3,"doc-seo-118301-105":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},118301,7971461740909,"Levi","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Revealing the Impacting Factors for the Adoption of Federated Machine Learning in Organizations","Machine Learning advances are constrained by digitized data that remains fragmented in silos, limiting both access and economic value under privacy and regulatory pressure. Federated Machine Learning enables model training on decentralized, potentially siloed data without direct sharing, yet many initiatives fail to reach production outcomes. To address this adoption gap, an interview study with 13 experts across seven organizations applies the Technology-Organization-Environment framework and derives 19 influencing factors to support managerial decisions and reduce implementation pitfalls.","Proceedings of the 57th Hawaii International Conference on System Sciences | 2024  \nRevealing the Impacting Factors for the Adoption of Federated Machine Learning in Organizations  \nTobias M¨uller  \nTechnical University of Munich and SAP SE [tobias.mueller15@sap.com](tobias.mueller15@sap.com)  \nMilena Zahn  \nTechnical University of Munich and SAP SE[milena.zahn@sap.com](milena.zahn@sap.com)  \nFlorian Matthes Technical University of Munich  \n[matthes@tum.de](matthes@tum.de)  \nAbstract  \nThe success of Machine Learning is driven by the ever-increasing wealth of digitized data. Still, a significant amount of the world’s data is scattered and locked in data silos, which leaves its full potential and therefore economic value largely untapped. Federated Machine Learning is a novel model-to-data approach that enables the training of Machine Learning modelson decentralized, potentially siloed data. Despite its potential, most Federated Machine Learning projects fail to actualize. The current literature lacks an understanding of the crucial factors for the adoption of Federated Machine Learning in organizations. We conducted an interview study with 13 experts from seven organizations to close this research gap. Specifically, we draw on the Technology-Organization-Environment framework and identified a total of 19 influencing factors. Thereby, we intend to facilitate managerial decision-making, aid practitioners in avoiding pitfalls, and thereby ease the successful implementation of Federated Machine Learning projects.  \nKeywords: Federated Machine Learning, Technology Adoption, TOE Framework, Interview Study  \n1. Introduction  \nThe ever-increasing wealth of digitized data powers the disruptive potential of Machine Learning (ML) and its immense economic impact. Even though vast amounts of data is freely available, extensive amounts of already generated data is scattered, stored, and locked up in decentralized devices and data silos. Accessing these data silos becomes more difficult with privacy concerns and legal regulations, which leaves the economic potential of the stored data largely untapped.  \nFederated Machine Learning (FedML) is a novel ML paradigm, with the promise to build prediction models on decentralized data without the need for direct data sharing (McMahan et al., 2016) . Through its model-to-data approach, FedML enables the usage of siloed data without disclosing data to third parties. Therefore, FedML has the potential to overcome data silos, enable the usage of currently untapped data and thereby be the catalyst for novel application fields of ML. Despite its advantages, there are only a few production-level applications and most work on FedML comprises prototypes or simulations (Lo et al., 2021) . Investigating the challenges, success factors, and influential factors for the adoption ofFedML might offer valuable insights into the missing operationalization of FedML. A better understanding of these factors would also aid practitioners to implement FedML projects and thereby support its broader practical adoption.  \nIn contrast to the literature on FedML, research on traditional, centralized Artificial Intelligence (AI) systems already provides relevant insights into the challenges and success factors of AI adoption. For example, research on AI adoption in the financial services industry recognized a lack of AI-related skills, missing top management support, market regulations, and complex implementation as the main challenges (Kruse et al., 2019) . Similar studies in the manufacturing and production domain identified leadership support as a crucial success factor (Demlehner and Laumer, 2020) . Besides, the complexity of an organization additionally hinders AI adoption in manufacturing firms (Chatterjee et al., 2021) . Similar results have also been obtained for AI adoption in public organizations (Neumann et al., 2022) .  \nOrganizations that are relatively inexperienced in AI technologies depend on the initiatives of s","cbCaivGLCfEd46uc","https://ap.wps.com/l/cbCaivGLCfEd46uc","pdf",226609,1,10,"English","en",105,"# Introduction\n## Federated Machine Learning and data silos\n## Adoption challenges from traditional AI research\n## Collaboration-related complexity in FedML","[{\"question\":\"Why is data silos a problem for machine learning value?\",\"answer\":\"Large amounts of digitized data are scattered and locked in data silos, and privacy concerns plus legal regulations make accessing them harder. This leaves much of the economic potential of stored data untapped.\"},{\"question\":\"What is federated machine learning and how does it address data silos?\",\"answer\":\"Federated Machine Learning trains models on decentralized data using a model-to-data approach, avoiding direct data sharing with third parties. This can enable usage of otherwise siloed data.\"},{\"question\":\"How does the study identify factors for adopting federated machine learning?\",\"answer\":\"The research conducts interviews with 13 experts from seven organizations and applies the Technology-Organization-Environment framework. It identifies 19 influencing factors to guide managerial decision-making and practitioner implementation.\"}]","Revealing the Impacting Factors for the Adoption of Federated Machine Learning in Organizations | PDF",1785682912,25,{"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},"revealing-the-impacting-factors-for-the-adoption-of-federated-machine-learning-in-organizations","",{"@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/revealing-the-impacting-factors-for-the-adoption-of-federated-machine-learning-in-organizations/118301/",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},"Why is data silos a problem for machine learning value?","Question",{"text":75,"@type":76},"Large amounts of digitized data are scattered and locked in data silos, and privacy concerns plus legal regulations make accessing them harder. This leaves much of the economic potential of stored data untapped.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What is federated machine learning and how does it address data silos?",{"text":80,"@type":76},"Federated Machine Learning trains models on decentralized data using a model-to-data approach, avoiding direct data sharing with third parties. This can enable usage of otherwise siloed data.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the study identify factors for adopting federated machine learning?",{"text":84,"@type":76},"The research conducts interviews with 13 experts from seven organizations and applies the Technology-Organization-Environment framework. 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