Zone-based Federated Learning for Mobile Sensing Data

Xiaopeng Jiang, Thinh On, Nhathai Phan, Hessamaldin Mohammadi, Vijaya Datta Mayyuri, An Chen, Ruoming Jin, Cristian Borcea

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

1 Scopus citations

Abstract

This paper proposes Zone-based Federated Learning (ZoneFL) to simultaneously achieve good model accuracy while adapting to user mobility behavior, scaling well as the number of users increases, and protecting user data privacy. ZoneFL divides the physical space into geographical zones mapped to a mobile-edge-cloud system architecture for good model accuracy and scalability. Each zone has a federated training model, called a zone model, which adapts well to data and behaviors of users in that zone. Benefiting from the FL design, the user data privacy is protected during the ZoneFL training. We propose two novel zone-based federated training algorithms to optimize zone models to user mobility behavior: Zone Merge and Split (ZMS) and Zone Gradient Diffusion (ZGD). ZMS optimizes zone models by adapting the zone geographical partitions through merging of neighboring zones or splitting of large zones into smaller ones. Different from ZMS, ZGD maintains fixed zones and optimizes a zone model by incorporating the gradients derived from neighboring zones' data. ZGD uses a self-Attention mechanism to dynamically control the impact of one zone on its neighbors. Extensive analysis and experimental results demonstrate that ZoneFL significantly outperforms traditional FL in two models for heart rate prediction and human activity recognition. In addition, we developed a ZoneFL system using Android phones and AWS cloud. The system was used in a heart rate prediction field study with 63 users for 4 months, which demonstrated the feasibility of ZoneFL in real-life.

Original languageEnglish (US)
Title of host publication2023 IEEE International Conference on Pervasive Computing and Communications, PerCom 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages141-148
Number of pages8
ISBN (Electronic)9781665453783
DOIs
StatePublished - 2023
Event21st IEEE International Conference on Pervasive Computing and Communications, PerCom 2023 - Atlanta, United States
Duration: Mar 13 2023Mar 17 2023

Publication series

Name2023 IEEE International Conference on Pervasive Computing and Communications, PerCom 2023

Conference

Conference21st IEEE International Conference on Pervasive Computing and Communications, PerCom 2023
Country/TerritoryUnited States
CityAtlanta
Period3/13/233/17/23

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Instrumentation

Keywords

  • edge computing
  • federated learning
  • mobile sensing
  • smart phones

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