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Federated Learning in the Enterprise: Leveraging decentralized data

Data quality is essential to machine learning and more – and more representative – training data is better. In a large organization, data often cannot be curated in a single data lake for privacy, regulatory or practical reasons. It might reside in data centers in different countries, in separate business units or grown application silos. Federated Learning (FL) is an approach to machine learning in which the training data is not managed centrally. Data is retained in locations that participate in the FL process and is never shared. This approach is just starting to be used in enterprises. This session discusses the basic approach of federated learning, how it can be applied both to neural networks but also to classical methods, from linear models to XGBoost, and how to make it work in practice in an enterprise.
Federated learning is now available in Watson Studio and Watson Machine Learning as a beta. However, this session talks about concepts, application, and where it fits in a company. There will be separate detailed tutorial on how to use federated learning with CloudPak for Data.