ERAFL: Efficient Resource Allocation for Federated Learning Training in Smart Homes

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Abstract

With the growing number of Federated Learning (FL) applications in smart homes, it becomes crucial to manage communication and computation resources within the smart home so that FL applications can complete their training on time. While computation offloading has relieved the challenge of timely completion of applications in case of high competition for local resources, privacy of the smart home data remains a critical concern. This paper introduces ERAFL, a resource allocation and computation offloading algorithm running on a home gateway. Unlike privacy-oblivious prior works, ERAFL considers privacy-sensitivity level of FL training data in offloading decision, prioritizing local processing of more sensitive data, e.g., biological personal data. Moreover, in case of insufficient local resources, ERAFL offloads a part of data and accelerates training by leveraging parallel training on the cloud and the edge device. It also imposes limits on the amount of offloaded data or performs the training either locally or remotely to ensure model accuracy. Our simulation results show that ERAFL can satisfy more FL training tasks and reduce data privacy leakage in comparison to the baselines that do not consider partial offloading, privacy sensitivity of application data or resource allocation.

Original languageEnglish
Title of host publicationNOMS 2024-2024 IEEE Network Operations and Management Symposium
EditorsJames Won-Ki Hong, Seung-Joon Seok, Yuji Nomura, You-Chiun Wang, Baek-Young Choi, Myung-Sup Kim, Roberto Riggio, Meng-Hsun Tsai, Carlos Raniery Paula dos Santos
PublisherIEEE
ISBN (Electronic)9798350327939
DOIs
Publication statusPublished - 2 Jul 2024
EventIEEE/IFIP Network Operations and Management Symposium, NOMS 2024 - Seoul, Korea, Republic of
Duration: 6 May 202410 May 2024
https://noms2024.ieee-noms.org

Conference

ConferenceIEEE/IFIP Network Operations and Management Symposium, NOMS 2024
Abbreviated titleNOMS 2024
Country/TerritoryKorea, Republic of
CitySeoul
Period6/05/2410/05/24
Internet address

Keywords

  • 2024 OA procedure
  • Offloading
  • Smart Home
  • Federated Learning

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