Federated learning: Applications, challenges and future directions

TL;DR

This paper provides a comprehensive overview of federated learning, emphasizing its applications in healthcare, privacy-preserving techniques, challenges, and future research directions, highlighting its potential to enable decentralized machine learning while protecting data privacy.

Contribution

It offers a detailed survey of federated learning frameworks, privacy methods, applications, and unresolved challenges, especially in healthcare, with insights into future research avenues.

Findings

FL enables privacy-preserving collaborative learning across distributed data.

Various privacy techniques like homomorphic encryption and differential privacy are employed.

Key challenges include communication costs, system heterogeneity, and privacy protection.

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.