Federated Learning: Applications, Challenges and Future Scopes
- URL: http://arxiv.org/abs/2205.09513v1
- Date: Wed, 18 May 2022 10:47:09 GMT
- Title: Federated Learning: Applications, Challenges and Future Scopes
- Authors: Subrato Bharati, M. Rubaiyat Hossain Mondal, Prajoy Podder, V. B.
Surya Prasath
- Abstract summary: Federated learning (FL) is a system in which a central aggregator coordinates the efforts of multiple clients to solve machine learning problems.
FL has applications in wireless communication, service recommendation, intelligent medical diagnosis systems, and healthcare.
- Score: 1.3190581566723918
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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 (DL) model via a central aggregator server. This
paper examines recent developments and provides a comprehensive list of
unresolved issues, inspired by the rapid growth of FL research. In the context
of FL, several privacy methods are described, including secure multiparty
computation, homomorphic encryption, differential privacy, and stochastic
gradient descent. Furthermore, a review of various FL classes, such as
horizontal and vertical FL and federated transfer learning, is provided. FL has
applications in wireless communication, service recommendation, intelligent
medical diagnosis systems, and healthcare, all of which are discussed in this
paper. We also present a thorough review of existing FL challenges, such as
privacy protection, communication cost, system heterogeneity, and unreliable
model upload, followed by future research directions.
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