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Quantum Machine Learning

Adaptive Aggregation with Two Gains in QFL

arXiv

Authors: S Nanayakkara

Year

2025

Paper ID

16282

Status

Preprint

Abstract Read

~2 min

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Abstract

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.

Why This Paper Matters

This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
It adds a 2025 reference point for readers tracking recent quantum research.
Federated learning (FL) deployed over quantum enabled and heterogeneous classical networks faces significant performance degradation due to uneven client quality, stochastic...

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References & Citation Signals

[1] DOI https://doi.org/arXiv:2512.03363 [2] arXiv https://arxiv.org/abs/2512.03363 [3] Publisher https://arxiv.org/abs/2512.03363

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