Quantum Federated Learning With Decentralized Data

Huang, Rui; Tan, Xiaoqing; Xu, Qingshan

doi:10.1109/jstqe.2022.3170150

Cited by 26 publications

(7 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the protocol, clients use differential privacy technology to protect the uploaded gradients and share the aggregated gradients in a distributed way. Huang et al [27] proposed variational quantum algorithm based on quantum federated learning. In the scheme, an extension of the conventional variational quantum algorithm is developed to find approximate optima in the parameter landscape.…”

Section: Related Workmentioning

confidence: 99%

“…The privacy protection of quantum machine learning is an emergent field and there is a lot of research about it. Most of the existing literature protects privacy by means of quantum homomorphic encryption [15][16][17][18][19][20][21][22], quantum differential privacy [23,24] and quantum secure multi-party computing [25][26][27]. Each method achieves privacy protection in different ways and presents different levels of security and availability.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Quantum neural network with privacy protection of input data and training parameters

Fang,

Chang

2024

Phys. Scr.

View full text Add to dashboard Cite

It takes a lot of computational resources to train a machine learning model. So does quantum machine learning. In the NISQ (Noisy Intermediate Scale Quantum) era, there is a need for users who cannot afford quantum computers to utilize quantum servers to complete quantum machine learning with protection privacy of data and model parameters. In this paper, novel homomorphic encryption methods and circuit design with hidden parameter for R z , R x and R y gate are putting forward. Based on which, we further propose quantum neural network (QNN) with privacy protection of input data and training parameters, where hiding model parameters has hardly mentioned in existing literature. To verify the validity of the proposed scheme, we conducted experiments on MNIST and Iris datasets, and obtained losses and accuracy that are almost identical to the original algorithms. Experiments show that our scheme can protect privacy well in the training and have no effect on accuracy of the algorithm. By comparison, the proposed scheme is more secure than most existing literature and requires less extra complexity O(4n).

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Quantum neural network with privacy protection of input data and training parameters

Fang,

Chang

2024

Phys. Scr.

View full text Add to dashboard Cite

show abstract

“…It has been demonstrated that the proposed framework achieved efficient training on independent and identically distributed (IID) and Non-IID distributed datasets. Huang et al [32] proposed the communication efficient QFL framework based on variational quantum tensor networks, quantum approximate optimization algorithm, and variational quantum eigensolver. It has been observed that the proposed framework reduced the possible attacks on client data and gained an exceptional performance in variational tasks.…”

Section: Related Workmentioning

confidence: 99%

Federated quanvolutional neural network: a new paradigm for collaborative quantum learning

Bhatia,

Kais,

Alam

2023

Quantum Sci. Technol.

View full text Add to dashboard Cite

In recent years, the concept of federated machine learning has been actively driven by scientists to ease the privacy concerns of data owners. Currently, the combination of machine learning and quantum computing technologies is a hot industry topic and is positioned to be a major disruptor. It has become an effective new tool for reshaping several industries ranging from healthcare to finance. Data sharing poses a significant hurdle for large-scale machine learning in numerous industries. It is a natural goal to study the advanced quantum computing ecosystem, which will be comprised of heterogeneous federated resources. In this work, the problem of data governance and privacy is handled by developing a quantum federated learning approach, that can be efficiently executed on quantum hardware in the noisy intermediate-scale quantum (NISQ) era. We present the federated hybrid quantumclassical algorithm called a quanvolutional neural network with distributed training on different sites without exchanging data. The hybrid algorithm requires small quantum circuits to produce meaningful features for image classification tasks, which makes it ideal for near-term quantum computing. The primary goal of this work is to evaluate the potential benefits of hybrid quantum-classical and classical-quantum convolutionalneural networks on non-independently and non-identically partitioned (Non-IID) and real-world data partitioned datasets among several healthcare institutions/clients. We investigated the performance of a collaborative quanvolutional neural network on two medical machine learning datasets, COVID-19 and MedNIST. Extensive experiments are carried out to validate the robustness and feasibility of the proposed quantum federated learning framework. Our findings demonstrate a decrease of 2-39% times in necessary communication rounds compared to federated stochastic gradient descent approach. The hybrid federated framework maintained a high classification testing accuracy and generalizability, even in scenarios where the medical data is unevenly distributed among clients.

show abstract

“…This can enhance the modeling of the algorithm for better performance extraction. FL can also be combined with quantum computing to create a learning model without violating data privacy, which is studied in [51]. Optimal quantum key distribution (QKD) protocol selection and intruder detection during the QKD process can be executed by employing ML algorithms [52], [53].…”

Section: ) Quantum Computingmentioning

confidence: 99%

A Comprehensive Review on Artificial Intelligence/Machine Learning Algorithms for Empowering the Future IoT Toward 6G Era

et al. 2022

View full text Add to dashboard Cite

The evolution of the wireless network systems over decades has been providing new services to the users with the help of innovative network and device technologies. In recent times, the 5G network systems are about to be deployed which creates the opportunity to realize massive connectivity with high throughput, low latency, high energy efficiency and security. It also focuses on providing massive Internet of Things (IoT) network connectivity as well as services for good health, large-scale agricultural and industrial production, intelligent traffic control and electricity generation, transmission and distribution systems. However, the ever-increasing number of user devices is directing the researchers towards beyond 5G systems to allocate these user devices with higher bandwidth. Researches on the 6G wireless network systems have already begun to provide higher bandwidth availability for densely connected larger network devices with QoS surety. Researchers are leveraging artificial intelligence (AI)/machine learning (ML) for enhancing future IoT network operations and services. This paper attempts to discuss AI/ML algorithms that can help in developing energy efficient, secured and effective IoT network operations and services. In particular, our article concentrates on the major issues and factors that influence the design of the communication systems for future IoT with the integration of AI/ML. It also highlights application domains, including smart healthcare, smart agriculture, smart transportation, smart grid and smart industry that can operate efficiently and securely. Finally, this paper ends with the discussion on future research scopes with these algorithms in addressing the open issues of the future IoT network systems.

show abstract

Quantum Federated Learning With Decentralized Data

Cited by 26 publications

References 28 publications

Quantum neural network with privacy protection of input data and training parameters

Quantum neural network with privacy protection of input data and training parameters

Federated quanvolutional neural network: a new paradigm for collaborative quantum learning

A Comprehensive Review on Artificial Intelligence/Machine Learning Algorithms for Empowering the Future IoT Toward 6G Era

Contact Info

Product

Resources

About