vFedProtoQNAS: Prototype-Guided Personalized Quantum Neural Architecture Search for Virtual Federated Learning
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Abstract (arXiv)
Quantum federated learning (QFL) has emerged as a promising approach for collaboratively training compact quantum neural networks (QNNs) over distributed private data on resource-constrained devices. However, differences in device capabilities make a single shared QNN architecture unsuitable for all clients. While personalized quantum neural architecture search (QNAS) allows each client to select a device-specific QNN, averaging parameters across structurally different QNN architectures mixes semantically inconsistent circuit operations. To address this, prototype-guided personalized QNAS for virtual FL (vFedProtoQNAS) is proposed, where model parameters are never aggregated across clients and federated collaboration is achieved through class-wise prototype sharing. Each client independently searches and trains a client-specific QNN, computes class-wise local prototypes from latent representations, and refines them using global prototypes from the server as federated semantic anchors. Experiments demonstrate that vFedProtoQNAS improves accuracy by 3.70% over FedAvg and enhances class-consistent representation alignment.
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References · 13
- 2021FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropoutcited by 192
- 2022Federated Learning from Pre-Trained Models: A Contrastive Learning Approachcited by 141
- 2022FedRolex: Model-Heterogeneous Federated Learning with Rolling Sub-Model Extractioncited by 102
- 2021Federated Quantum Machine Learningcited by 82
- 2021Quantum Federated Learning with Quantum Datacited by 53
- 2023Foundations of Quantum Federated Learning Over Classical and Quantum Networkscited by 21
- 2024Federated Model Heterogeneous Matryoshka Representation Learningcited by 21
- 2024An Upload-Efficient Scheme for Transferring Knowledge From a Server-Side Pre-trained Generator to Clients in Heterogeneous Federated Learningcited by 17
- 2023FedSSA: Semantic Similarity-based Aggregation for Efficient Model-Heterogeneous Personalized Federated Learningcited by 10
- 2023NeFL: Nested Model Scaling for Federated Learning with System Heterogeneous Clientscited by 7
- 2023Fast Quantum Convolutional Neural Networks for Low-Complexity Object Detection in Autonomous Driving Applicationscited by 5
- 2023SuperFedNAS: Cost-Efficient Federated Neural Architecture Search for On-Device Inferencecited by 4
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