Adaptive federated learning in resource constrained edge computing systems

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Title: Adaptive federated learning in resource constrained edge computing systems
Authors: Wang, S
Tuor, T
Salonidis, T
Leung, KK
Makaya, C
He, T
Chan, K
Item Type: Journal Article
Abstract: Emerging technologies and applications including Internet of Things (IoT), social networking, and crowd-sourcing generate large amounts of data at the network edge. Machine learning models are often built from the collected data, to enable the detection, classification, and prediction of future events. Due to bandwidth, storage, and privacy concerns, it is often impractical to send all the data to a centralized location. In this paper, we consider the problem of learning model parameters from data distributed across multiple edge nodes, without sending raw data to a centralized place. Our focus is on a generic class of machine learning models that are trained using gradientdescent based approaches. We analyze the convergence bound of distributed gradient descent from a theoretical point of view, based on which we propose a control algorithm that determines the best trade-off between local update and global parameter aggregation to minimize the loss function under a given resource budget. The performance of the proposed algorithm is evaluated via extensive experiments with real datasets, both on a networked prototype system and in a larger-scale simulated environment. The experimentation results show that our proposed approach performs near to the optimum with various machine learning models and different data distributions.
Issue Date: 31-Dec-2019
Date of Acceptance: 1-Jan-2019
ISSN: 0733-8716
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Journal / Book Title: IEEE Journal on Selected Areas in Communications
Copyright Statement: © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Sponsor/Funder: IBM United Kingdom Ltd
Funder's Grant Number: 4603317662
Keywords: 0906 Electrical and Electronic Engineering
1005 Communications Technologies
0805 Distributed Computing
Networking & Telecommunications
Publication Status: Published online
Online Publication Date: 2019-03-11
Appears in Collections:Computing
Electrical and Electronic Engineering

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