[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-116963-en":3,"doc-seo-116963-105":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},116963,34359740700684,"Finn","https://ap-avatar.wpscdn.com/avatar/1f400023980c374ae676?_k=1777273430885731487",8,"Research & Report","A Pathway for the Practical Adoption of Federated Machine Learning Projects - Completed Research Paper","Big data underpins machine learning success, but large portions of digitized information remain trapped in data silos, leaving their value unrealized. Federated Machine Learning enables decentralized model training via a model-to-data approach, offering a path to overcome siloed access barriers. Yet most federated projects stall because of their decentralized structure and insufficiently connected development steps. This study addresses the gap by proposing three activity models using a design science approach to guide practitioners through the federated machine learning project lifecycle, reducing complexity and easing implementation.","Association for Information Systems  \nAIS Electronic Library (AISeL)  \n\n| PACIS 2023 Proceedings | Pacific Asia Conference on Information Systems (PACIS) |\n| --- | --- |\n| 7-8-2023\u003Cbr>A Pathway for the Practical Adoption of Federated Machine Learning Projects\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/pacis2023](https://aisel.aisnet.org/pacis2023) |  |\n\nRecommended Citation  \nMüller, Tobias; Zahn, Milena; and Matthes, Florian, \"A Pathway for the Practical Adoption of Federated Machine Learning Projects\" (2023) . PACIS 2023 Proceedings. 6.  \n[https://aisel.aisnet.org/pacis2023/6](https://aisel.aisnet.org/pacis2023/6)  \nThis material is brought to you by the Pacific Asia Conference on Information Systems (PACIS) at AIS Electronic Library (AISeL) . It has been accepted for inclusion in PACIS 2023 Proceedings by an authorized administrator of AIS Electronic Library (AISeL) . For more information, please [contact elibrary@aisnet.org](contact elibrary@aisnet.org).  \nPathway for theAdoption ofFedML Projects  \nA Pathway for the Practical Adoption of Federated Machine Learning Projects  \nCompleted Research Paper  \nTobias Müller  \nTechnical University of Munich and SAP SE Munich, Germany [tobias1.mueller@tum.de](tobias1.mueller@tum.de)  \nMilena Zahn  \nTechnical University of Munich and SAP SE Munich, Germany [milena.zahn@tum.de](milena.zahn@tum.de)  \nFlorian Matthes  \nTechnical University of Munich  \nMunich, Germany  \n[matthes@tum.de](matthes@tum.de)  \nAbstract  \nBig data forms the fundamental basis for the success of Machine Learning. Yet, a large amount of the world’s digitized data is locked up in data silos, leaving its potential untapped. Federated Machine Learning is a novel Machine Learning paradigm with the potential to overcome data silos by enabling the decentralized training of Machine Learning models through a model-to-data approach. Despite its potential advantages, most Federated Machine Learning projects fail to actualize due to their decentralized structure and incomprehensive interrelations. Current literature lacks clear guidelines on which steps need to be performed to successfully implement Federated Machine Learning projects. This study aims to close this research gap. Through a design science research approach, we provide three distinct activity models which outline required tasks in the development of Federated Machine Learning systems. Thereby, we aim to reduce complexity and ease the implementation process by guiding practitioners through the project life cycle.  \nKeywords: Federated Machine Learning, Activity Model, Software Engineering, AI.  \nIntroduction  \nThe success of Machine Learning (ML) systems roots in the emergence of big data and the ever-increasing availability and wealth of digitized information. Even though data forms the fundamental basis for powering ML systems, it also poses ML’s major bottleneck. Problem domains become increasingly complex, which results in more sophisticated and therefore data-demanding ML systems. The development of such advanced, intelligent systems is often restricted through a lack of sufficient training data. Especially small and medium-sized businesses (SMEs) experience this problem and suffer from a deficiency of training data (Bauer et al., 2020) . Although a considerable amount of data is freely available, vast amounts of the world’s data is scattered in decentralized IoT devices and data silos. The siloed data is usually hardly accessible to external prospective parties, which leaves a significant portion of generated data largely untapped. By breaking up these data silos through collaboration and data sharing, SMEs could overcome the persisting problem of data scarcity","cbCaiaY04g9MZ4cj","https://ap.wps.com/l/cbCaiaY04g9MZ4cj","pdf",665476,1,17,"English","en",105,"# Introduction\n## Data silos and machine learning bottlenecks\n## Federated machine learning and its promise\n## Why federated projects fail to reach production","[{\"question\":\"What problem does the paper address in federated machine learning projects?\",\"answer\":\"Most federated machine learning projects fail to become production-ready because their decentralized structure and interrelations are not sufficiently addressed, and clear implementation guidelines are missing.\"},{\"question\":\"How does federated machine learning help with data silos?\",\"answer\":\"It enables decentralized training using a model-to-data approach, bringing the model to where data resides so training data does not need to leave individual devices.\"},{\"question\":\"What does the study contribute to practitioners?\",\"answer\":\"Using a design science research approach, it provides three activity models that outline required tasks across the federated machine learning system development life cycle to reduce complexity and support implementation.\"}]","A Pathway for the Practical Adoption of Federated Machine Learning Projects - 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