Personalized Learning with Limited Data on Edge Devices Using Federated Learning and Meta-Learning

Kousalya Soumya Lahari Voleti, Shen Shyang Ho

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

Abstract

The efficient and effective handling of few-shot learning tasks on mobile devices is challenging due to the small training set issue and the physical limitations in power and computational resources on these devices. We propose a framework that combines federated learning and meta-learning to handle independent few-shot learning tasks on multiple devices. In particular, we utilize the Prototypical Networks to perform meta-learning on all devices to learn multiple independent few-shot learning models and to aggregate the device models using federated learning which can be reused by the devices subsequently. We perform extensive experiments to (1) compare three different federated learning approaches, namely Federated Averaging (FedAvg), Federated Proximal (FedProx), and Federated Personalization (FedPer) on the proposed framework, and (2) investigate the effect of data heterogeneity issue on multiple devices on their few-shot learning performance. Our empirical results show that our proposed framework is feasible and is able to improve the devices' individual prediction performance and significant performance improvement using the aggregated model using any of the federated learning approaches when the few-shot learning tasks are from the same source and data heterogeneity continues to be a challenging issue to overcome.

Original languageEnglish (US)
Title of host publicationProceedings - 2023 IEEE/ACM Symposium on Edge Computing, SEC 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages378-382
Number of pages5
ISBN (Electronic)9798400701238
DOIs
StatePublished - 2023
Externally publishedYes
Event8th Annual IEEE/ACM Symposium on Edge Computing, SEC 2023 - Wilmington, United States
Duration: Dec 6 2023Dec 9 2023

Publication series

NameProceedings - 2023 IEEE/ACM Symposium on Edge Computing, SEC 2023

Conference

Conference8th Annual IEEE/ACM Symposium on Edge Computing, SEC 2023
Country/TerritoryUnited States
CityWilmington
Period12/6/2312/9/23

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Networks and Communications
  • Hardware and Architecture
  • Computer Science Applications

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