Multi-Level ACE-based IoT Knowledge Sharing for Personalized Privacy-Preserving Federated Learning

Jing Wang*, Xi Lin, Jun Wu, Qinghua Mao, Bei Pei, Jianhua Li, Suchang Guo, Baitao Zhang

*Corresponding author for this work

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

Abstract

The emerging federated learning (FL) enables distributed data mining for Internet of Things (IoT) big data while avoiding data outsourcing privacy risks via local data training and knowledge (i.e., model) sharing. However, only simplified local knowledge sharing will also cause user privacy leaks due to advanced attacks (e.g., model inversion or gradient leakage). Further, how to realize fine-grained and personalized privacy protection for IoT users is still a challenge. In this paper, we first propose a hierarchical cloud-edge orchestrated federated learning architecture for IoT, named HCE-FL, which aims to provide intelligent and distributed data analysis for IoT users. To address the FL privacy issues, we then design a multi-level access control encryption-based IoT knowledge sharing approach for HCE-FL. In our approach, IoT users could be classified into different levels according to their individual privacy requirements. In addition, the proposed multi-level access control encryption algorithm could ensure the confidentiality of the IoT knowledge flow, which runs through local clients, edge sanitizers, and cloud servers in HCE-FL. Moreover, security theoretical analysis shows that our HCE-FL could satisfy'no read' and no write' security rules for the mandatory IoT knowledge access control. Finally, we conduct experiments based on classic MNIST and CIFARIO datasets to evaluate our HCE-FL. The experimental results demonstrate that our solution can achieve personalized privacy-preserving FL without losing IoT data availability and users can obtain better model accuracy and convergence rate through secure IoT knowledge access and sharing.

Original languageEnglish
Title of host publicationProceedings - 2023 19th International Conference on Mobility, Sensing and Networking, MSN 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages843-848
Number of pages6
ISBN (Electronic)9798350358261
DOIs
Publication statusPublished - 2023
Externally publishedYes
Event19th International Conference on Mobility, Sensing and Networking, MSN 2023 - Jiangsu, China
Duration: 2023 Dec 142023 Dec 16

Publication series

NameProceedings - 2023 19th International Conference on Mobility, Sensing and Networking, MSN 2023

Conference

Conference19th International Conference on Mobility, Sensing and Networking, MSN 2023
Country/TerritoryChina
CityJiangsu
Period23/12/1423/12/16

Keywords

  • federated learning
  • Internet of Things
  • knowledge sharing
  • privacy protection

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing
  • Information Systems and Management
  • Control and Optimization
  • Instrumentation

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