[en] Abstract
Federated learning (FL) is a machine learning technique that distributes model training to multiple clients while allowing clients to keep their data local. Although the technique allows one to break free from data silos keeping data local, to coordinate such distributed training, it requires an orchestrator, usually a central server. Consequently, organisational issues of governance might arise and hinder its adoption in both competitive and collaborative markets for data. In particular, the question of how to govern FL applications is recurring for practitioners. This research commentary addresses this important issue by inductively proposing a layered decision framework to derive organisational archetypes for FL’s governance. The inductive approach is based on an expert workshop and post-workshop interviews with specialists and practitioners, as well as the consideration of real-world applications. Our proposed framework assumes decision-making occurs within a black box that contains three formal layers: data market, infrastructure, and ownership. Our framework allows us to map organisational archetypes ex-ante. We identify two key archetypes: consortia for collaborative markets and in-house deployment for competitive settings. We conclude by providing managerial implications and proposing research directions that are especially relevant to interdisciplinary and cross-sectional disciplines, including organisational and administrative science, information systems research, and engineering.
Research center :
Interdisciplinary Centre for Security, Reliability and Trust (SnT) > FINATRAX - Digital Financial Services and Cross-organizational Digital Transformations
Disciplines :
Computer science Management information systems
Author, co-author :
Barbereau, Tom ; Dutch Organization for Applied Scientific Research (TNO), The Hague, The Netherlands ; Institute for Information Law (IViR), University of Amsterdam, Amsterdam, The Netherlands
FNR13342933 - DFS - Paypal-fnr Pearl Chair In Digital Financial Services, 2019 (01/01/2020-31/12/2024) - Gilbert Fridgen FNR17886330 - DELPHI - Data Driven Electricity Load Prediction For Households And Small Industry, 2023 (01/10/2023-30/09/2025) - Gilbert Fridgen
T.B. is supported by TNO under the Early Research Program “Next Generation Cryptography.” J.D.F. and
S.P.M. are supported by the Luxembourg National Research Fund (FNR) and PayPal, PEARL grant reference 13,342,933/Gilbert
Fridgen, and by FNR grant reference HPC BRIDGES/2022_Phase2/17886330/DELPHI. For the purpose of open access and in fulfilling the obligations arising from the grant agreement, the authors have applied a Creative Commons Attribution 4.0
International (CC BY 4.0) license to any Author Accepted Manuscript version arising from this submission
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