Akademik Veri Yönetim Sistemi
Real-Time Intelligence for Heterogeneous Networks, Fadi Al-Turjman, Editör, Springer Nature, Zug, ss.19-42, 2021
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.