Federated Learning: Organizational Opportunities, Challenges, and Adoption Strategies
It addresses data privacy challenges for organizations in industries with strict data regulations, but is incremental as it builds on existing federated learning concepts without introducing new technical methods.
The paper explores federated learning as a solution to restrictive data-sharing rules, presenting a conceptual framework for organizational adoption across various sectors like public authorities and financial services.
Restrictive rules for data sharing in many industries have led to the development of federated learning. Federated learning is a machine-learning technique that allows distributed clients to train models collaboratively without the need to share their respective training data with others. In this paper, we first explore the technical foundations of federated learning and its organizational opportunities. Second, we present a conceptual framework for the adoption of federated learning, mapping four types of organizations by their artificial intelligence capabilities and limits to data sharing. We then discuss why exemplary organizations in different contexts - including public authorities, financial service providers, manufacturing companies, as well as research and development consortia - might consider different approaches to federated learning. To conclude, we argue that federated learning presents organizational challenges with ample interdisciplinary opportunities for information systems researchers.