Engineering Federated Learning Systems: A Literature Review
Paper in proceedings, 2021

With the increasing attention on Machine Learning applications, more and more companies are involved in implementing AI components into their software products in order to improve the service quality. With the rapid growth of distributed edge devices, Federated Learning has been introduced as a distributed learning technique, which enables model training in a large decentralized network without exchanging collected edge data. The method can not only preserve sensitive user data privacy but also save a large amount of data transmission bandwidth and the budget cost of computation equipment. In this paper, we provide a state-of-the-art overview of the empirical results reported in the existing literature regarding Federated Learning. According to the problems they expressed and solved, we then categorize those deployments into different application domains, identify their challenges and then propose six open research questions.

Federated learning

Machine learning

Literature review

Software business

Author

Hongyi Zhang

Chalmers, Computer Science and Engineering (Chalmers), Software Engineering (Chalmers), Software Engineering for Testing, Requirements, Innovation and Psychology

Jan Bosch

Chalmers, Computer Science and Engineering (Chalmers), Software Engineering (Chalmers), Software Engineering for Testing, Requirements, Innovation and Psychology

Helena Holmström Olsson

Malmö university

Lecture Notes in Business Information Processing

1865-1348 (ISSN)

Vol. 407 210-218

11th International Conference on Software Business, ICSOB 2020
Karlskrona, Sweden,

Subject Categories

Other Computer and Information Science

Computer Science

Computer Systems

DOI

10.1007/978-3-030-67292-8_17

More information

Latest update

3/18/2021