EdgeC3: Online Management for Edge-Cloud Collaborative Continuous Learning

Shaohui Lin, Xiaoxi Zhang*, Yupeng Li, Carlee Joe-Wong, Jingpu Duan, Xu Chen

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

Abstract

Deep learning (DL) powered real-time applications usually need continuous training using data streams generated geographically. Enabling data offloading among computation nodes through model training is promising to mitigate the problem that devices generating large datasets may have low computation capability. However, offloading can compromise model convergence and incur communication costs, which must be balanced with the cost spent on computation and model synchronization. Therefore, this paper proposes EdgeC3, a novel framework that can optimize the frequency of model aggregation and dynamic offloading for continuously generated data streams, navigating the trade-off between long-term accuracy and cost. We first provide a new error bound to capture the impacts of data dynamics that are varying over time and heterogeneous across devices. Based on the bound, we design a two-timescale online optimization framework. We periodically learn the synchronization frequency to adapt with uncertain future offloading and network changes. In the finer timescale, we manage online offloading by extending Lyapunov optimization techniques to handle an unconventional setting, where our long-term global constraint can have abruptly changed aggregation frequencies that are decided in the longer timescale. Finally, we theoretically prove the convergence of EdgeC3 by integrating the coupled effects of our two-timescale decisions, and we demonstrate its advantage through extensive experiments.

Original languageEnglish
Title of host publication2023 20th Annual IEEE International Conference on Sensing, Communication, and Networking, SECON 2023
Place of PublicationMadrid, Spain
PublisherIEEE
Pages411-419
Number of pages9
ISBN (Electronic)9798350300529
ISBN (Print)9798350300536
DOIs
Publication statusPublished - 11 Sept 2023
Event20th Annual IEEE International Conference on Sensing, Communication, and Networking, SECON 2023 - Madrid, Spain
Duration: 11 Sept 202314 Sept 2023
https://ieeexplore.ieee.org/xpl/conhome/10287388/proceeding (Conference proceedings)

Publication series

NameAnnual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks workshops
Volume2023-September
ISSN (Print)2155-5486
ISSN (Electronic)2155-5494

Conference

Conference20th Annual IEEE International Conference on Sensing, Communication, and Networking, SECON 2023
Country/TerritorySpain
CityMadrid
Period11/09/2314/09/23
Internet address

Scopus Subject Areas

  • Computer Networks and Communications
  • Hardware and Architecture
  • Electrical and Electronic Engineering

Fingerprint

Dive into the research topics of 'EdgeC3: Online Management for Edge-Cloud Collaborative Continuous Learning'. Together they form a unique fingerprint.

Cite this