Modeling and Performance Analysis on Federated Learning in Edge Computing

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

2 Scopus citations


Federated Learning (FL) deployed in edge computing may achieve some advantages such as private data protection, communication cost reduction, and lower training latency compared to cloud-centric training approaches. The Anything-as-a-Service (XaaS) paradigm, as the main service provisioning model in edge computing, enables various flexible FL deployments. On the other hand, the distributed nature of FL together with the highly diverse computing and networking infrastructures in an edge environment introduce extra latency that may degrade FL performance. Therefore, delay performance evaluation on edge-based FL systems becomes an important research topic. However, XaaS-based FL deployment brings new challenges to performance analysis that cannot be well addressed by conventional analytical approaches. In this paper, we attempt to address such challenges by proposing a profile-based modeling and analysis method for evaluating delay performance of edge-based FL systems. The insights obtained from the modeling and analysis may offer useful guidelines to various aspects of FL design. Application of network calculus techniques makes the proposed method general and flexible, thus may be applied to FL systems deployed upon the heterogeneous edge infrastructures.

Original languageEnglish (US)
Title of host publicationProceedings - 2021 IEEE World Congress on Services, SERVICES 2021
EditorsNimanthi Atukorala, Carl K. Chang, Rong N. Chang, Ernesto Damiani, Ian Foster, Zhonghie Wang, Jia Zhang
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781665427197
StatePublished - 2021
Event2021 IEEE World Congress on Services, SERVICES 2021 - Virtual, Online, United States
Duration: Sep 5 2021Sep 11 2021

Publication series

NameProceedings - 2021 IEEE World Congress on Services, SERVICES 2021


Conference2021 IEEE World Congress on Services, SERVICES 2021
Country/TerritoryUnited States
CityVirtual, Online

All Science Journal Classification (ASJC) codes

  • Health Informatics
  • Business and International Management
  • Artificial Intelligence
  • Computer Networks and Communications
  • Software
  • Information Systems and Management
  • Safety, Risk, Reliability and Quality


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