A Secure and Scalable Peer-to-Peer Federated Learning Approach for Handling Veracity in Big Data

Maria Iqbal, Asadullah Tariq, Mohamed Adel Serhani

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

Abstract

A massive source of terabytes of data is produced consistently from advanced data frameworks and innovation such as distributed/cloud computing, mobile network devices, and the Internet of Things. However, maintaining data veracity in handling such vast and diverse datasets presents significant challenges. In response to these challenges, Federated Learning (FL) emerges as a promising candidate to address the veracity issues in big data analytics. The distributed approach of FL not only ensures data privacy but also mitigates the risk of data breaches and unauthorized access, enhancing data security and veracity. In this paper, we present a Secure and Scalable Peer-to-Peer FL (P2P-FL), a novel approach that tackles the challenges of maintaining data veracity while simultaneously enhancing security and scalability through the utilization of multi-level encryption-decryption processes. This approach eliminates the need for a central server and allows federates to communicate directly, optimizing the efficiency of FL. Differential privacy techniques further ensure individual data privacy. Overall, our approach advances the effectiveness and security of machine learning models in privacy-sensitive environments. In our study, we conducted a comprehensive analysis traditional FL and Peer-to-Peer P2P-FL. We evaluated the performance based on accuracy, loss, and F1 score metrics. The results showcased the effectiveness of our Secure and Scalable Peer-to-Peer FL approach.

Original languageEnglish
Title of host publication2023 IEEE International Conference on Dependable, Autonomic and Secure Computing, International Conference on Pervasive Intelligence and Computing, International Conference on Cloud and Big Data Computing, International Conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages456-462
Number of pages7
ISBN (Electronic)9798350304602
DOIs
Publication statusPublished - 2023
Event2023 IEEE International Conference on Dependable, Autonomic and Secure Computing, 2023 International Conference on Pervasive Intelligence and Computing, 2023 International Conference on Cloud and Big Data Computing, 2023 International Conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2023 - Abu Dhabi, United Arab Emirates
Duration: Nov 14 2023Nov 17 2023

Publication series

Name2023 IEEE International Conference on Dependable, Autonomic and Secure Computing, International Conference on Pervasive Intelligence and Computing, International Conference on Cloud and Big Data Computing, International Conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2023

Conference

Conference2023 IEEE International Conference on Dependable, Autonomic and Secure Computing, 2023 International Conference on Pervasive Intelligence and Computing, 2023 International Conference on Cloud and Big Data Computing, 2023 International Conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2023
Country/TerritoryUnited Arab Emirates
CityAbu Dhabi
Period11/14/2311/17/23

Keywords

  • Artificial Intelligence
  • Big Data
  • Federated Learning
  • Mobile Networks
  • Unstructured Data
  • Veracity

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Hardware and Architecture
  • Information Systems
  • Information Systems and Management
  • Safety, Risk, Reliability and Quality
  • Control and Optimization

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