Adaptive Aggregation with Two Gains in QFL

TL;DR

This paper proposes A2G, an adaptive aggregation framework for quantum federated learning that improves performance by jointly optimizing geometric blending and client importance based on network quality metrics.

Contribution

It introduces a novel dual gain framework, A2G, that adapts aggregation in quantum federated learning considering network heterogeneity and quantum-specific challenges.

Findings

Enhanced aggregation accuracy in quantum federated systems

Improved robustness against network heterogeneity

Effective modulation of client influence based on QoS metrics

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.