Abstract
Quantum federated learning (QFL) is a quantum extension of the classical federated learning model across multiple local quantum devices. An efficient optimization algorithm is always expected to minimize the communication overhead among different quantum participants. In this work, we propose an efficient optimization algorithm, namely federated quantum natural gradient descent (FQNGD), and further, apply it to a QFL framework that is com-posed of a variational quantum circuit (VQC)-based quantum neural networks (QNN). Compared with stochastic gradient descent methods like Adam and Adagrad, the FQNGD algorithm admits much fewer training iterations for the QFL to get converged. Moreover, it can significantly reduce the total communication overhead among local quantum devices. Our experiments on a handwritten digit classification dataset justify the effectiveness of the FQNGD for the QFL framework in terms of a faster convergence rate on the training set and higher accuracy on the test set.
Original language | English |
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Title of host publication | ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing |
Publisher | IEEE |
Number of pages | 5 |
Edition | 1st |
ISBN (Electronic) | 9781728163277 |
ISBN (Print) | 9781728163284 |
DOIs | |
Publication status | Published - 4 Jun 2023 |
Event | 48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023 - Rhodes Island, Greece Duration: 4 Jun 2023 → 10 Jun 2023 https://ieeexplore.ieee.org/xpl/conhome/10094559/proceeding |
Publication series
Name | ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings |
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Volume | 2023-June |
ISSN (Print) | 1520-6149 |
ISSN (Electronic) | 2379-190X |
Conference
Conference | 48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023 |
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Country/Territory | Greece |
City | Rhodes Island |
Period | 4/06/23 → 10/06/23 |
Internet address |
Scopus Subject Areas
- Software
- Signal Processing
- Electrical and Electronic Engineering
User-Defined Keywords
- Quantum neural network
- variational quantum circuit
- quantum federated learning
- federated quantum natural gradient descent