Adaptive Aggregation with Two Gains in QFL
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Abstract (arXiv)
Federated learning (FL) deployed over quantum enabled and heterogeneous classical networks faces significant performance degradation due to uneven client quality, stochastic teleportation fidelity, device instability, and geometric mismatch between local and global models. Classical aggregation rules assume euclidean topology and uniform communication reliability, limiting their suitability for emerging quantum federated systems. This paper introduces A2G (Adaptive Aggregation with Two Gains), a dual gain framework that jointly regulates geometric blending through a geometry gain and modulates client importance using a QoS gain derived from teleportation fidelity, latency, and instability.
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References · 13
- 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
- 2018Federated Optimization in Heterogeneous Networkscited by 2,971
- 2020Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimizationcited by 897
- 2020Variational Quantum Algorithmscited by 687
- 2020The power of quantum neural networkscited by 377
- 2019Efficient Learning for Deep Quantum Neural Networkscited by 160
- 2021Federated Quantum Machine Learningcited by 82
- 2016Riemannian SVRG: Fast Stochastic Optimization on Riemannian Manifoldscited by 64
- 2023Optimizing Quantum Federated Learning Based on Federated Quantum Natural Gradient Descentcited by 14
- 2023Riemannian stochastic optimization methods avoid strict saddle pointscited by 8
- 2020Riemannian Adaptive Optimization Algorithm and Its Application to Natural Language Processingcited by 6
- 2024Nonconvex Federated Learning on Compact Smooth Submanifolds With Heterogeneous Datacited by 5
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