Naslov
Dew Intelligence: Federated learning perspective

Autori
Guberović, Emanuel ; Lipić, Tomislav ; Čavrak, Igor

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
2021 IEEE 45th Annual Computers, Software, and Applications Conference (COMPSAC) / - , 2021, 1819-1824

Skup
IEEE 45th Annual Computers, Software, and Applications Conference (COMPSAC)

Mjesto i datum
Online, 12.07.2021. - 16.07.2021

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
dew computing ; distributed computing ; federated learning ; collaborative learning ; machine learning

Sažetak
Newly emerging and evolving technologies such as Cloud, Fog and Edge Computing, as well as Internet of Things, Cyber-Physical Systems and Distributed Ledger Technology (such as blockchain) together with advances in Artificial Intelligence (AI) research are increasingly becoming a common and pervasive phenomenon in our everyday lives. Their co-evolution with society is driving the emergence of future socio-technical systems, which further promote ubiquitous entanglement between humans and machines. Fog, Edge and Dew computing as post-Cloud computing paradigms aim to relocate computing resources closer to end users in order to mitigate cloud-specific issues of highly centralized computation. Dew computing as the youngest of the post-cloud paradigms promotes human centered independence and collaboration between devices within scalable distributed computing infrastructures. Meanwhile, the field of artificial intelligence is adapting to recent challenges posed by user data privacy regulations as well as opportunities for applications on mobile devices based on their growing computational abilities. The usage of artificial intelligence in pervasive and scalable distributed computing systems is a natural step towards ubiquitous intelligent infrastructures and collaborative human and machine environments. Federated learning is an artificial intelligence technique enabling collaborative learning in distributed devices environment without sharing the training data sets, which are often private. This paper provides the overview of the federated learning paradigm showing that it inherently leverages both independence and collaboration, thus exemplifying implementation of dew intelligence within scalable distributed computing hierarchy.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo

POVEZANOST RADA