Adaptive Aggregation with Two Gains in QFL
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908838781779968 |
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| author | Nanayakkara, S |
| author_facet | Nanayakkara, S |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_03363 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Adaptive Aggregation with Two Gains in QFL Nanayakkara, S Machine Learning Quantum Physics 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. |
| title | Adaptive Aggregation with Two Gains in QFL |
| topic | Machine Learning Quantum Physics |
| url | https://arxiv.org/abs/2512.03363 |