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Reputation-Aware Multi-Agent DRL for Secure Hierarchical Federated Learning in IoT

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journal contribution
submitted on 2023-12-13, 06:52 and posted on 2023-12-14, 10:29 authored by Noora Mohammed Al-Maslamani, Mohamed Abdallah, Bekir Sait Ciftler

Aiming at protecting device data privacy, Federated Learning (FL) is a framework of distributed machine learning in which devices’ local model parameters are exchanged with a centralized server without revealing the actual data. Hierarchical Federated Learning (HFL) framework was introduced to improve FL communication efficiency where devices are clustered and seek model consensus with the support of edge servers (e.g., base stations). Devices in a cluster submit their local model updates to their assigned local edge server for aggregation at each iteration. The edge servers transmit the aggregated models to a centralized server and establish a global consensus. However, similar to FL, adversaries may threaten the security and privacy of HFL. The client devices within a cluster may deliberately provide unreliable local model updates through poisoning attacks or poor-quality model updates due to inconsistent communication channels, increased device mobility, or inadequate device resources. To address the above challenges, this paper investigates the client selection problem in the HFL framework to eliminate the impact of unreliable clients while maximizing the global model accuracy of HFL. Each FL edge server is equipped with a Deep Reinforcement Learning (DRL)-based reputation model to optimally measure the reliability and trustworthiness of FL workers within its cluster. A Multi-Agent Deep Deterministic Policy Gradient (MADDPG) is utilized to enhance the accuracy and stability of the HFL global model, given the workers’ dynamic behaviors in the HFL environment. The experimental results indicate that our proposed MADDPG improves the accuracy and stability of HFL compared with the conventional reputation model and single-agent DDPG-based reputation model.

Other Information

Published in: IEEE Open Journal of the Communications Society
License: https://creativecommons.org/licenses/by/4.0/
See article on publisher's website: https://dx.doi.org/10.1109/ojcoms.2023.3280359

Funding

Open Access funding provided by the Qatar National Library.

History

Language

  • English

Publisher

IEEE

Publication Year

  • 2023

License statement

This Item is licensed under the Creative Commons Attribution 4.0 International License.

Institution affiliated with

  • Hamad Bin Khalifa University
  • College of Science and Engineering - HBKU
  • University of Doha for Science and Technology
  • College of Computing and Information Technology - UDST