Quantum Federated Learning

Latest papers

13 papers3 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

vFedProtoQNAS: Prototype-Guided Personalized Quantum Neural Architecture Search for Virtual Federated Learning
Oct 1, 2026Seok Bin Son, Samuel Yen-Chi Chen, Soohyun Park +1Quantum Machine Learning Quantum Neural Networks
Korea University Seoul, Korea · Wells Fargo New York, USA · Sookmyung Women’s University Seoul, Korea
Encryptability As a Coordinate Choice: Depth-One Homomorphic Federated Learning of Quantum Neural Networks
Sep 24, 2026Marcel Mordarski, Nathan Mani, Arshad Patel +2Quantum Federated Learning Privacy-Preserving ML
Department of Computing, Imperial College London London SW7 2AZ, United Kingdom
Federating Quantum and Classical Computing: A Privacy-Preserving Hybrid Approach
Sep 18, 2026Carlos Cano, Daniel M. Jimenez-Gutierrez, Diego Sal +4Privacy-Preserving Federated Learning Quantum Federated Learning
Quantum Federated Learning Based on Bures--Uhlmann Geometry for Heterogeneous Noisy Clients
Aug 28, 2026Haruki Emori, Masaki Uchihara, Yuuki TokunagaQuantum Federated Learning Quantum Machine Learning
Graduate School of Information Science and Technology, Hokkaido University, Kita 14, Nishi 9, Kita-ku, Sapporo, Hokkaido 060-0814, Japan · RIKEN Center for Interdisciplinary Theoretical and Mathematical Sciences (iTHEMS), RIKEN, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan · Department of Diabetes, Endocrinology, and Metabolism, National Center for Global Health and Medicine, Japan Institute for Health Security, 1-21-1 Toyama, Shinjuku-ku, Tokyo 162-8655, Japan
Dynamic Entanglement-Weighted Pruning for Quantum Federated Unlearning in Supply-Chain Risk Prediction
Aug 17, 2026Aditya Kumar, Sumit ChongderQuantum Federated Learning Federated Unlearning
IDRP - Quantum Information and Computation, Department of Physics & Computer Science and Engineering, Indian Institute of Technology Jodhpur, Jodhpur, Rajasthan 342030, India
PAS-QFL: Personalized Ansatz Selection for Quantum Federated Learning under Client Data Heterogeneity
Aug 15, 2026Jindi Wu, Qun LiQuantum Federated Learning Federated Learning
School of computing DePaul University Chicago, USA · Computer science department William & Mary Williamsburg, USA
A Drift Stable Quantum Federated Learning for Intelligent Services
Jul 22, 2026Shanika Iroshi Nanayakkara, Shiva Raj PokhrelQuantum Machine Learning Deep Unfolding
School of IT, Deakin University, VIC 3125, Burwood, Australia
QFedAgent: Quantum-Enhanced Personalized Federated Learning for Multi-Agent Activity Recognition
Jul 2, 2026Quoc Bao Phan, Tuy Tan NguyenNon-IID Federated Learning Human Activity Recognition
Department of Electrical and Computer Engineering FAMU-FSU College of Engineering, Florida State University Tallahassee, FL 32310, USA
Q-ANCHOR: Federated Quantum Learning with ZNE-guided Correction
May 28, 2026Hoang M. Ngo, Quan Nguyen, Wanli Xing +1Federated Learning Aggregation Quantum Machine Learning
Department of Computer & Information Science & Engineering University of Florida · Frost Institute for Data Science and Computing University of Miami
Can Quantum Federated Learning Withstand Circuit-Level Backdoors?
May 18, 2026Aakar Mathur, Mohammed Ruknuddin, Ashish GuptaBackdoor Attacks in Federated Learning Quantum Machine Learning
BITS Pilani Dubai Campus, Dubai, UAE
Modeling Quantum Federated Autoencoder for Anomaly Detection in IoT Networks
Mar 23, 2026Devashish Chaudhary, Sutharshan Rajasegarar, Shiva Raj PokhrelQuantum Federated Learning Anomaly Detection
School of Information Technology, Deakin University, Geelong, Australia
Zero-Knowledge Federated Learning with Lattice-Based Hybrid Encryption for Quantum-Resilient Medical AI
Mar 3, 2026Edouard LansiauxQuantum Federated Learning Privacy-Preserving Federated Learning
Department of Emergency Medicine & STaR-AI Research Group, CHU de Lille, Université de Lille, Lille, France
Communication-Efficient Quantum Federated Learning over Large-Scale Wireless Networks
Mar 1, 2026Shaba Shaon, Christopher G. Brinton, Dinh C. NguyenQuantum Federated Learning Wireless Resource Allocation
ECE Department, The University of Alabama in Huntsville, Huntsville, AL 35899, USA · Elmore Family School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, 47907, USA

Oct 1, 2026J/K move · Enter open · S save

vFedProtoQNAS: Prototype-Guided Personalized Quantum Neural Architecture Search for Virtual Federated Learning

Seok Bin Son, Samuel Yen-Chi Chen, Soohyun Park, Joongheon Kim

Korea University Seoul, Korea · Wells Fargo New York, USA · Sookmyung Women’s University Seoul, Korea

Quantum Machine Learning Quantum Neural Networks Federated Learning Heterogeneous Federated Learning Quantum Federated Learning

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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.