Optimization of CNN-based Federated Learning for Cyber-Physical Detection

Authors

Ammar Kamal Abasi, Mohamed Bin Zayed University of Artificial IntelligenceFollow
Moayad Aloqaily, Mohamed Bin Zayed University of Artificial IntelligenceFollow
Bassem Ouni, Technology Innovation Institute
Maher Hamdi, The George Washington University

Document Type

Conference Proceeding

Publication Title

Proceedings - IEEE Consumer Communications and Networking Conference, CCNC

Abstract

With the increasing popularity of Cyber-physical Systems (CPS), there is a growing need for efficient and reliable methods for detecting and responding to threats. Federated Learning (FL) is a distributed Machine Learning (ML) technique that can be used to train models on data from multiple devices (i.e., edge devices) while keeping the data local. FL has the potential to improve the security and privacy of data while also reducing the training time and cost. Particularly, CNN-based FL has been shown to be effective for various tasks such as image classification and object detection. However, selecting suitable hyperparameters for constructing local ML models in FL is a significant challenge for practical inference and training on edge devices. In this paper, we focus on the optimization of CNN-based federated learning for the task of cyber-physical detection and we propose employing a novel metaheuristic optimization algorithm called Honey Badger Algorithm (HBA) for tuning the hyperparameters in local ML models (FL-HBA). To show the effectiveness of FL-HBA, we make an evaluation using an intelligent healthcare case study where we consider Sleep Apnea (SA) and use the PhysioNet apnea ECG dataset to diagnose SA. Our results show that the FL-HBA is superior to a Convolutional Neural Network (CNN) baseline, traditional ML techniques, and centralized learning models. Furthermore, we demonstrate that the proposed method for assigning the near-optimal hyperparameter values for centralized learning models improves accuracy by 2%.

First Page

1

Last Page

6

DOI

10.1109/CCNC51644.2023.10059840

Publication Date

3-17-2023

Keywords

CNN Hyper-parameter, Cyber-physical Systems (CPS), Federated Learning (FL), Honey Badger Algorithm (HBA), Sleep Apnea (SA)

Comments

IR conditions: non-described

Recommended Citation

A. K. Abasi, M. Aloqaily, B. Ouni and M. Hamdi, "Optimization of CNN-based Federated Learning for Cyber-Physical Detection," 2023 IEEE 20th Consumer Communications & Networking Conference (CCNC), Las Vegas, NV, USA, 2023, pp. 1-6, Mar 2023, doi: 10.1109/CCNC51644.2023.10059840.