Graduate School and Research Center in Digital Sciences

Optimal number of edge devices in distributed learning over wireless channels

Song, Jaeyoung; Kountouris, Marios

SPAWC 2020, IEEE 21st International Workshop on Signal Processing Advances in Wireless Communications, Special Session on Distributed Learning and Control for Future Wireless Networks, 26-29 May 2020, Atlanta, GA, USA (Virtual Conference)

We consider a distributed learning system, where a parameter server (PS) assigns data and computational tasks to edge devices to build a global model. Distributing data to multiple workers involves communication between PS and edge devices and entails a fundamental tradeoff between computation and communication. In this paper, we aim at characterizing the optimal number of edge devices required for guaranteeing convergence and for achieving a certain accuracy within a finite time horizon.

Document Bibtex

Title:Optimal number of edge devices in distributed learning over wireless channels
Type:Conference
Language:English
City:Atlanta
Country:UNITED STATES
Date:
Department:Communication systems
Eurecom ref:6256
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Bibtex: @inproceedings{EURECOM+6256, year = {2020}, title = {{O}ptimal number of edge devices in distributed learning over wireless channels}, author = {{S}ong, {J}aeyoung and {K}ountouris, {M}arios}, booktitle = {{SPAWC} 2020, {IEEE} 21st {I}nternational {W}orkshop on {S}ignal {P}rocessing {A}dvances in {W}ireless {C}ommunications, {S}pecial {S}ession on {D}istributed {L}earning and {C}ontrol for {F}uture {W}ireless {N}etworks, 26-29 {M}ay 2020, {A}tlanta, {GA}, {USA} ({V}irtual {C}onference)}, address = {{A}tlanta, {UNITED} {STATES}}, month = {05}, url = {http://www.eurecom.fr/publication/6256} }
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