Affiliations: [a] Institute of Information and Communication Technology, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh | [b] Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA | [c] Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA | [d] Department of Biomedical Informatics, College of Medicine, University of Cincinnati, Cincinnati, OH, USA | [e] Department of Electrical Engineering and Computer Science, University of Cincinnati, Cincinnati, OH, USA
Correspondence:
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Corresponding author: Subrato Bharati, Institute of Information and Communication Technology, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh. E-mail: subratobharati1@gmail.com.
Abstract: Federated learning (FL) refers to a system in which a central aggregator coordinates the efforts of several clients to solve the issues of machine learning. This setting allows the training data to be dispersed in order to protect the privacy of each device. This paper provides an overview of federated learning systems, with a focus on healthcare. FL is reviewed in terms of 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. Inspired by the rapid growth of FL research, this paper examines recent developments and provides a comprehensive list of unresolved issues. Several privacy methods including secure multiparty computation, homomorphic encryption, differential privacy and stochastic gradient descent are described in the context of FL. Moreover, a review is provided for different classes of FL such as horizontal and vertical FL and federated transfer learning. FL has applications in wireless communication, service recommendation, intelligent medical diagnosis system and healthcare, which we review in this paper. We also present a comprehensive review of existing FL challenges for example privacy protection, communication cost, systems heterogeneity, unreliable model upload, followed by future research directions.
