@inproceedings{Leung:2018:10.1109/INFOCOM.2018.8486403,
author = {Leung, KK and Wang, S and Tuor, T and Salonidis, T and Makaya, C and He, T and Chan, K},
doi = {10.1109/INFOCOM.2018.8486403},
publisher = {IEEE},
title = {When edge meets learning: adaptive control for resource-constrained distributed machine learning},
url = {http://dx.doi.org/10.1109/INFOCOM.2018.8486403},
year = {2018}
}

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TY  - CPAPER
AB  - Emerging  technologies  and  applications  includingInternet of Things (IoT), social networking, and crowd-sourcinggenerate  large  amounts  of  data  at  the  network  edge.  Machinelearning models are often built from the collected data, to enablethe   detection,   classification,   and   prediction   of   future   events.Due  to  bandwidth,  storage,  and  privacy  concerns,  it  is  oftenimpractical to send all the data to a centralized location. In thispaper,  we  consider  the  problem  of  learning  model  parametersfrom data distributed across multiple edge nodes, without sendingraw  data  to  a  centralized  place.  Our  focus  is  on  a  generic  classof  machine  learning  models  that  are  trained  using  gradient-descent  based  approaches.  We  analyze  the  convergence  rate  ofdistributed  gradient  descent  from  a  theoretical  point  of  view,based on which we propose a control algorithm that determinesthe  best  trade-off  between  local  update  and  global  parameteraggregation to minimize the loss function under a given resourcebudget. The performance of the proposed algorithm is evaluatedvia extensive experiments with real datasets, both on a networkedprototype  system  and  in  a  larger-scale  simulated  environment.The  experimentation  results  show  that  our  proposed  approachperforms  near  to  the  optimum  with  various  machine  learningmodels  and  different  data  distributions.
AU  - Leung,KK
AU  - Wang,S
AU  - Tuor,T
AU  - Salonidis,T
AU  - Makaya,C
AU  - He,T
AU  - Chan,K
DO  - 10.1109/INFOCOM.2018.8486403
PB  - IEEE
PY  - 2018///
TI  - When edge meets learning: adaptive control for resource-constrained distributed machine learning
UR  - http://dx.doi.org/10.1109/INFOCOM.2018.8486403
UR  - http://hdl.handle.net/10044/1/58765
ER  - 

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