Resource-Efficient and Delay-Aware Federated Learning Design under Edge Heterogeneity

David Nickel, Frank Po Chen Lin, Seyyedali Hosseinalipour, Nicolo Michelusi, Christopher G. Brinton

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

Abstract

Federated learning (FL) has emerged as a popular technique for distributing machine learning across wireless edge devices. We examine FL under two salient properties of contemporary networks: device-server communication delays and device computation heterogeneity. Our proposed StoFedDelAv algorithm incorporates a local-global model combiner into the FL synchronization step. We theoretically characterize the convergence behavior of StoFedDelAv and obtain the optimal combiner weights, which consider the global model delay and expected local gradient error at each device. We then formulate a network-aware optimization problem which tunes the minibatch sizes of the devices to jointly minimize energy consumption and machine learning training loss, and solve the non-convex problem through a series of convex approximations. Our simulations reveal that StoFedDelAv outperforms the current art in FL, evidenced by the obtained improvements in optimization objective.

Original languageEnglish (US)
Title of host publication2022 IEEE International Conference on Communications Workshops, ICC Workshops 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages43-48
Number of pages6
ISBN (Electronic)9781665426718
DOIs
StatePublished - 2022
Externally publishedYes
Event2022 IEEE International Conference on Communications Workshops, ICC Workshops 2022 - Seoul, Korea, Republic of
Duration: May 16 2022May 20 2022

Publication series

Name2022 IEEE International Conference on Communications Workshops, ICC Workshops 2022

Conference

Conference2022 IEEE International Conference on Communications Workshops, ICC Workshops 2022
Country/TerritoryKorea, Republic of
CitySeoul
Period5/16/225/20/22

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Networks and Communications
  • Signal Processing
  • Control and Optimization

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