AutoFL: Enabling heterogeneity-aware energy efficient federated learning

Young Geun Kim, Carole Jean Wu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Federated learning enables a cluster of decentralized mobile devices at the edge to collaboratively train a shared machine learning model, while keeping all the raw training samples on device. This decentralized training approach is demonstrated as a practical solution to mitigate the risk of privacy leakage. However, enabling efficient FL deployment at the edge is challenging because of non-IID training data distribution, wide system heterogeneity and stochastic-varying runtime effects in the field. This paper jointly optimizes time-toconvergence and energy efficiency of state-of-the-art FL use cases by taking into account the stochastic nature of edge execution. We propose AutoFL by tailor-designing a reinforcement learning algorithm that learns and determines which K participant devices and per-device execution targets for each FL model aggregation round in the presence of stochastic runtime variance, system and data heterogeneity. By considering the unique characteristics of FL edge deployment judiciously, AutoFL achieves 3.6 times faster model convergence time and 4.7 and 5.2 times higher energy efficiency for local clients and globally over the cluster of K participants, respectively.

Original languageEnglish (US)
Title of host publicationMICRO 2021 - 54th Annual IEEE/ACM International Symposium on Microarchitecture, Proceedings
PublisherIEEE Computer Society
Pages183-198
Number of pages16
ISBN (Electronic)9781450385572
DOIs
StatePublished - Oct 18 2021
Event54th Annual IEEE/ACM International Symposium on Microarchitecture, MICRO 2021 - Virtual, Online, Greece
Duration: Oct 18 2021Oct 22 2021

Publication series

NameProceedings of the Annual International Symposium on Microarchitecture, MICRO
ISSN (Print)1072-4451

Conference

Conference54th Annual IEEE/ACM International Symposium on Microarchitecture, MICRO 2021
Country/TerritoryGreece
CityVirtual, Online
Period10/18/2110/22/21

Keywords

  • Energy efficiency
  • Federate learning
  • Heterogeneity
  • Mobile devices
  • Reinforcement learning

ASJC Scopus subject areas

  • Hardware and Architecture

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