Exact Decomposition of Quantum Channels for Non-IID Quantum Federated
Learning
- URL: http://arxiv.org/abs/2209.00768v1
- Date: Fri, 2 Sep 2022 00:38:44 GMT
- Title: Exact Decomposition of Quantum Channels for Non-IID Quantum Federated
Learning
- Authors: Haimeng Zhao
- Abstract summary: Federated learning refers to the task of performing machine learning with decentralized data from multiple clients while protecting data security and privacy.
We show that when the clients' data are not independent and identically distributed (IID), the performance of conventional federated algorithms deteriorates.
We prove that a global quantum channel can be exactly decomposed into channels trained by each client with the help of local density estimators.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Federated learning refers to the task of performing machine learning with
decentralized data from multiple clients while protecting data security and
privacy. Works have been done to incorporate quantum advantage in such
scenarios. However, when the clients' data are not independent and identically
distributed (IID), the performance of conventional federated algorithms
deteriorates. In this work, we explore this phenomenon in the quantum regime
with both theoretical and numerical analysis. We further prove that a global
quantum channel can be exactly decomposed into channels trained by each client
with the help of local density estimators. It leads to a general framework for
quantum federated learning on non-IID data with one-shot communication
complexity. We demonstrate it on classification tasks with numerical
simulations.
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