Robust Decentralized Federated Learning Using Collaborative Decisions

Document Type

Conference Proceeding

Publication Title

2022 International Wireless Communications and Mobile Computing

Abstract

Federated Learning (FL) has attracted a lot of attention in numerous applications due to recent data privacy regulations and increased awareness about data handling issues, combined with the ever-increasing big-data sizes. This paper proposes a server-less, robust FL training mechanism that allows any set of participating data-owners to train a neural network (NN) model collaboratively without the assistance of any central node and while being resilient to Byzantine attacks. The proposed approach makes use of a dual-way update mechanism to allow each node to take a model forwarding decision towards a global collaborative decision of isolating any malicious updates. The efficiency of the proposed approach in detecting cardiac irregularities is verified using simulation results conducted based on the Physikalisch-Technische Bundesanstalt Database electro-cardiogram (PTBDB ECG) dataset. © 2022 IEEE.

First Page

254

Last Page

258

DOI

10.1109/IWCMC55113.2022.9824826

Publication Date

7-19-2022

Keywords

Byzantine attacks, Decentralized Networks, Distributed Learning, E-health, Federated Learning, Neural network models

Comments

IR Deposit conditions: non-described

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