Federated learning framework for mobile edge computing networks. Issue 1 (8th January 2020)

• Record Type: Journal Article
• Authors: Fantacci, Romano; Picano, Benedetta
• Is Part Of: CAAI transactions on intelligence technology. Volume 5:Issue 1(2020)
• Page Start: 15
• Page End: 21
• Publication Date: 2020-01-08
• Subjects: data privacy -- neural nets -- learning (artificial intelligence) -- virtual machines -- mobile computing -- computer networks; federated learning framework -- mobile edge computing networks -- smart devices -- moving storage -- network edges -- edge computing paradigm -- edge computing nodes -- content requests -- prediction demand -- classical prediction approaches -- personal users -- central unit -- learning procedures -- multiple users -- sensitive data -- application demand prediction problem -- popular application types -- high accuracy levels -- predicted applications demand -- local training process -- deep learning; B6210L Computer communications -- C1140Z Other topics in statistics -- C5620 Computer networks and techniques -- C6130S Data security -- C6170K Knowledge engineering techniques -- C6190V Mobile, ubiquitous and pervasive computing; Artificial intelligence -- Periodicals; Computer science -- Periodicals; Artificial intelligence; Computer science; Electronic journals; Periodicals; 006.305
• DOI: 10.1049/trit.2019.0049
• Languages: English
• ISSNs: 2468-6557

Abstract

Abstract : The continuous growth of smart devices needing processing has led to moving storage and computation from cloud to the network edges, giving rise to the edge computing paradigm. Owing to the limited capacity of edge computing nodes, the presence of popular applications in the edge nodes results in significant improvements in users' satisfaction and service accomplishment. However, the high variability in the content requests makes prediction demand not trivial and, typically, the majority of the classical prediction approaches require the gathering of personal users' information at a central unit, giving rise to many users' privacy issues. In this context, federated learning gained attention as a solution to perform learning procedures from data disseminated across multiple users, keeping the sensitive data protected. This study applies federated learning to the demand prediction problem, to accurately forecast the more popular application types in the network. The proposed framework reaches high accuracy levels on the predicted applications demand, aggregating in a global and weighted model the feedback received by users, after their local training. The validity of the proposed approach is verified by performing a virtual machine replica copies and comparison with the alternative forecasting approach based on chaos theory and deep learning.