Date

2021-10-01

Author

Koçyiğit, Altan

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

342
views

0
downloads

Cite This

With the recent advancements in heterogeneous networks, particularly following the improvements in the Internet of Things (IoT) supporting infrastructures, various machine learning applications which use distributed computing facilities such as cloud, fog, and edge computing have gained popularity. One way of performing computationally intensive learning-related tasks is through distributed machine learning. Due to certain privacy-related concerns, it may not be possible to collect data representative enough to fit a generalisable machine learning model. In such cases, decentralised approaches such as federated learning become a viable option. Federated learning techniques can be used effectively by employing large numbers of participants of heterogeneous nature in terms of computational and storage resources, communication interfaces, as well as types and volumes of available data. In this chapter, federated learning and related concepts are explored together with heterogeneous networks and heterogeneous objects. Enabling technologies such as cloud, fog, edge, and mobile edge computing facilities are discussed together with federated learning architectures. Existing challenges and open research issues are considered critically, taking the heterogeneous nature of federated learning into account, particularly for the applications in the field of IoT.

URI

https://link.springer.com/book/10.1007/978-3-030-75614-7#about
https://hdl.handle.net/11511/94710

Collections

Graduate School of Informatics, Book / Book chapter