Federated Learning for Cross-Industry Data Collaboration: Enhancing Privacy and Innovation
Abstract
A decentralized method of machine learning called federated learning (FL) enables several organizations to work together to build a model without exchanging raw data. This approach addresses the demand for innovation while also resolving privacy issues and presents a substantial opportunity for cross-industry data collaboration. The implementation of FL in cross-industry situations is examined in this study, with a focus on how it might improve privacy and promote creativity. We examine the body of research on FL, talk about how it's being used in different industries, and offer a plan for productive FL-based cross-industry cooperation. The results indicate that FL is a promising solution for cooperative efforts in data-driven sectors since it may foster innovation while upholding strict privacy rules.
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