Imperial College London
Alternate

ProfessorKinLeung

Faculty of Engineering, Department of Electrical and Electronic Engineering

Tanaka Chair in Internet Technology
 
 
 
//

Contact

 

+44 (0)20 7594 6238kin.leung Website

 
 
//

Assistant

 

Miss Vanessa Rodriguez-Gonzalez +44 (0)20 7594 6267

 
//

Location

 

810aElectrical EngineeringSouth Kensington Campus

//

Summary

 

Publications

Citation

BibTex format

@article{Wang:2019:10.1109/jsac.2019.2904348,
author = {Wang, S and Tuor, T and Salonidis, T and Leung, KK and Makaya, C and He, T and Chan, K},
doi = {10.1109/jsac.2019.2904348},
journal = {IEEE Journal on Selected Areas in Communications},
pages = {1205--1221},
title = {Adaptive federated learning in resource constrained edge computing systems},
url = {http://dx.doi.org/10.1109/jsac.2019.2904348},
volume = {37},
year = {2019}
}
Download

RIS format (EndNote, RefMan)

TY  - JOUR
AB  - 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.
AU  - Wang,S
AU  - Tuor,T
AU  - Salonidis,T
AU  - Leung,KK
AU  - Makaya,C
AU  - He,T
AU  - Chan,K
DO  - 10.1109/jsac.2019.2904348
EP  - 1221
PY  - 2019///
SN  - 0733-8716
SP  - 1205
TI  - Adaptive federated learning in resource constrained edge computing systems
T2  - IEEE Journal on Selected Areas in Communications
UR  - http://dx.doi.org/10.1109/jsac.2019.2904348
UR  - https://ieeexplore.ieee.org/document/8664630
UR  - http://hdl.handle.net/10044/1/69216
VL  - 37
ER  -
Download