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Federated Learning: Applications, Challenges and Future Scopes. (arXiv:2205.09513v1 [cs.LG])
May 20, 2022, 1:12 a.m. | Subrato Bharati, M. Rubaiyat Hossain Mondal, Prajoy Podder, V. B. Surya Prasath
cs.LG updates on arXiv.org arxiv.org
Federated learning (FL) is a system in which a central aggregator coordinates
the efforts of multiple clients to solve machine learning problems. This
setting allows training data to be dispersed in order to protect privacy. The
purpose of this paper is to provide an overview of FL systems with a focus on
healthcare. FL is evaluated here based on its frameworks, architectures, and
applications. It is shown here that FL solves the preceding issues with a
shared global deep learning …
applications arxiv challenges federated learning future learning
More from arxiv.org / cs.LG updates on arXiv.org
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