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Federated learning with distributed fixed design quantum chips and quantum channels

arXiv 2401.13421 ↗First posted 24 Jan 2024quant-ph cs.DC cs.LGcited by 0

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Abstract (arXiv)

The privacy in classical federated learning can be breached through the use of local gradient results combined with engineered queries to the clients. However, quantum communication channels are considered more secure because a measurement on the channel causes a loss of information, which can be detected by the sender. Therefore, the quantum version of federated learning can be used to provide better privacy. Additionally, sending an N-dimensional data vector through a quantum channel requires sending log N entangled qubits, which can potentially provide efficiency if the data vector is utilized as quantum states. In this paper, we propose a quantum federated learning model in which fixed design quantum chips are operated based on the quantum states sent by a centralized server. Based on the incoming superposition states, the clients compute and then send their local gradients as quantum states to the server, where they are aggregated to update parameters. Since the server does not send model parameters, but instead sends the operator as a quantum state, the clients are not required to share the model. This allows for the creation of asynchronous learning models. In addition, the model is fed into client-side chips directly as a quantum state; therefore, it does not require measurements on the incoming quantum state to obtain model parameters in order to compute gradients. This can provide efficiency over models where the parameter vector is sent via classical or quantum channels and local gradients are obtained through the obtained values these parameters.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 22 of its 71 references that are in the map. First posted on arXiv 24 Jan 2024.

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22 of 71 references.

References · 22

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2018Federated Learning with Non-IID Datacited by 1,108
  3. 2020Inverting Gradients -- How easy is it to break privacy in federated learning?cited by 712
  4. 2016Federated Optimization: Distributed Machine Learning for On-Device Intelligencecited by 564
  5. 2019Robust and Communication-Efficient Federated Learning from Non-IID Datacited by 419
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  7. 2017Gradient Sparsification for Communication-Efficient Distributed Optimizationcited by 201
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  9. 2021When the Curious Abandon Honesty: Federated Learning Is Not Privatecited by 127
  10. 2023Stochastic Controlled Averaging for Federated Learning with Communication Compressioncited by 101
  11. 2021Quantum machine learning beyond kernel methodscited by 92
  12. 2021Federated Quantum Machine Learningcited by 82
10 more
  1. 2019Stochastic gradient descent for hybrid quantum-classical optimizationcited by 59
  2. 2021Quantum Federated Learning with Quantum Datacited by 53
  3. 2021Quantum federated learning through blind quantum computingcited by 44
  4. 2022Slimmable Quantum Federated Learningcited by 23
  5. 2023Privacy-preserving quantum federated learning via gradient hidingcited by 15
  6. 2020Towards a Distributed Quantum Computing Ecosystemcited by 11
  7. 2020A Systematic Survey of General Sparse Matrix-Matrix Multiplicationcited by 10
  8. 2023Dimension reduction and redundancy removal through successive Schmidt decompositionscited by 3
  9. 2022Quantum Pseudoentanglementcited by 2
  10. 2018Quantum Advantage for the LOCAL Model in Distributed Computingcited by 1

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