Federated learning and genetic mutation for multi-resident activity recognition

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Abstract

Multi-Resident activity recognition refers to the task of recognizing activities performed by multiple individuals living in the same residence. It involves using sensors or other monitoring devices to capture data about the activities taking place in the living space, and then using Machine Learning (ML) or Deep Learning (DL) algorithms to analyze and classify these activities. Federated Learning (FL) is a technique that enables multiple devices to collaboratively train a model without sharing their data with each other, while Genetic Mutation (GM) is a technique used in evolutionary algorithms to introduce random changes to the genetic code of individuals in a population. Our proposed framework involves the use FL and GM for Human Activity Recognition (HAR). The approach was evaluated on the ARAS dataset, collected from two houses with different activity patterns. Two Recurrent Neural Network (RNN) models, Gated Recurrent Unit (GRU) and Long-Short Term Memory (LSTM), were employed for the activity classification task and a genetic mutation operator was applied to the weights of the models before federated averaging. The results indicate that FL is suitable for privacy preserving activity recognition, it can help with early deployment and even improve the performance of the models in some cases.
Original languageEnglish
Title of host publication2023 IEEE 19th International Conference on e-Science (e-Science)
EditorsGeorge Angelos Papadopoulos
PublisherIEEE
Number of pages6
ISBN (Electronic)9798350322231
ISBN (Print)9798350322248
DOIs
Publication statusPublished - 25 Sept 2023

Publication series

NameIEEE Conference Proceedings
PublisherIEEE
ISSN (Print)2325-372X
ISSN (Electronic)2325-3703

Keywords

  • activity recognition
  • multi-resident
  • deep learning
  • federated learning
  • genetic mutation
  • LSTM
  • GRU
  • ARAS

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