Multimodal Multiple Federated Feature Construction Method for IoT Environments

Afsaneh Mahanipour, Hana Khamfroush

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The fast development of Internet-of-Things (IoT) devices and applications has led to vast data collection, potentially containing irrelevant, noisy, or redundant features that degrade learning model performance. These collected data can be processed on either end-user devices (clients) or edge/cloud server. Feature construction is a pre-processing technique that can generate discriminative features and reveal hidden relationships between original features within a dataset, leading to improved performance and reduced computational complexity of learning models. Moreover, the communication cost between clients and edge/cloud server can be minimized in situations where a dataset needs to be transmitted for further processing. In this paper, the first federated feature construction (FFC) method called multi-modal multiple FFC (MMFFC) is proposed by using multimodal optimization and gravitational search programming algorithm. This is a collaborative method for constructing multiple high-level features without sharing clients' datasets to enhance the trade-off between accuracy of the trained model and overall communication cost of the system, while also reducing computational complexity of the learning model. We analyze and compare the accuracy-cost trade-off of two scenarios, namely, 1) MMFFC federated learning (FL), using vanilla FL with pre-processed datasets on clients and 2) MMFFC centralized learning, transferring pre-processed datasets to an edge server and using centralized learning model. The results on three datasets for the first scenario and eight datasets for the second one demonstrate that the proposed method can reduce the size of datasets for about 60%, thereby reducing communication cost and improving accuracy of the learning models tested on almost all datasets.

Original languageEnglish
Title of host publicationGLOBECOM 2023 - 2023 IEEE Global Communications Conference
Pages1890-1895
Number of pages6
ISBN (Electronic)9798350310900
DOIs
StatePublished - 2023
Event2023 IEEE Global Communications Conference, GLOBECOM 2023 - Kuala Lumpur, Malaysia
Duration: Dec 4 2023Dec 8 2023

Publication series

NameProceedings - IEEE Global Communications Conference, GLOBECOM
ISSN (Print)2334-0983
ISSN (Electronic)2576-6813

Conference

Conference2023 IEEE Global Communications Conference, GLOBECOM 2023
Country/TerritoryMalaysia
CityKuala Lumpur
Period12/4/2312/8/23

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

Keywords

  • Feature Construction
  • Federated Learning
  • Gravitational Search Programming
  • Internet-of-Things
  • Machine Learning

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Hardware and Architecture
  • Signal Processing

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