FedGPO: Heterogeneity-Aware Global Parameter optimization for Efficient Federated Learning

Young Geun Kim, Carole Jean Wu

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

Abstract

Federated learning (FL) has emerged as a solution to deal with the risk of privacy leaks in machine learning training. This approach allows a variety of mobile devices to collaboratively train a machine learning model without sharing the raw on-device training data with the cloud. However, efficient edge deployment of FL is challenging because of the system/data heterogeneity and runtime variance. This paper optimizes the energy-efficiency of FL use cases while guaranteeing model convergence, by accounting for the aforementioned challenges. We propose FedGPO based on a reinforcement learning, which learns how to identify optimal global parameters (B, E, K) for each FL aggregation round adapting to the system/data heterogeneity and stochastic runtime variance. In our experiments, FedGPO improves the model convergence time by 2.4 times, and achieves 3.6 times higher energy efficiency over the baseline settings, respectively.

Original languageEnglish (US)
Title of host publicationProceedings - 2022 IEEE International Symposium on Workload Characterization, IISWC 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages117-129
Number of pages13
ISBN (Electronic)9781665487986
DOIs
StatePublished - 2022
Event2022 IEEE International Symposium on Workload Characterization, IISWC 2022 - Austin, United States
Duration: Nov 6 2022Nov 8 2022

Publication series

NameProceedings - 2022 IEEE International Symposium on Workload Characterization, IISWC 2022

Conference

Conference2022 IEEE International Symposium on Workload Characterization, IISWC 2022
Country/TerritoryUnited States
CityAustin
Period11/6/2211/8/22

ASJC Scopus subject areas

  • Artificial Intelligence
  • Hardware and Architecture

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