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Communication-Efficient Private Federated Reinforcement Learning with Post-Quantum Authentication for Vehicular Networks — arXiv:2610.41055

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.41055
License (as archived): CC0-1.0
Archived: 2026-11-03T05:39:53.429Z
SHA-256 of the archived text: 70a0df5f86dac62a5a89da71fb7dd1b933903fb1c2f305b28e908f8748516fb8
Stable URL of this page: https://metraipolis.com/documento/dc_fd83cd6fe204

How to cite

Communication-Efficient Private Federated Reinforcement Learning with Post-Quantum Authentication for Vehicular Networks — arXiv:2610.41055 — arXiv (en), https://arxiv.org/abs/2610.41055 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_fd83cd6fe204 (Metraipolis archive dc_fd83cd6fe204, sha256 70a0df5f86dac62a5a89da71fb7dd1b933903fb1c2f305b28e908f8748516fb8)

BibTeX

@misc{dcfd83cd6fe204,
  title = {Communication-Efficient Private Federated Reinforcement Learning with Post-Quantum Authentication for Vehicular Networks — arXiv:2610.41055},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.41055},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_fd83cd6fe204; sha256 70a0df5f86dac62a5a89da71fb7dd1b933903fb1c2f305b28e908f8748516fb8},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.41055. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Federated reinforcement learning supports cooperative vehicular routing without centralizing trajectory data, but model updates may reveal private information and incur communication overhead over intermittent vehicle-to-infrastructure links, particularly when post-quantum authentication is required. We study correlated private quantization, using complementary shared randomness between paired vehicles to reduce aggregation mean squared error while preserving the marginal parameter-level local differential privacy and one-bit communication cost of independent quantization. A tunable extension mixes shared and independent randomness to bound privacy leakage given paired outputs and shared-randomness side information. For this extension, Rényi differential privacy accounting establishes vector-level local differential privacy for one model upload even when pairings are revealed. Under matched vector-level privacy and identical local and global parameters, the extension yields 16.5% lower analytical aggregation mean squared error than independent quantization. We evaluate cooperative routing by coupling traffic simulation with packet-level simulation of post-quantum-authenticated vehicle-to-infrastructure communication. Compared to full-precision updates, one-bit uploads reduce authenticated uplink size by 92.4% and increase mean verified updates aggregated per round from 19.6 to 26.5. Under mobility-constrained communication at a parameter-level privacy budget of 1, correlated private quantization reduces mean travel time by 14.3% relative to independent quantization at the same budget and communication cost. At a parameter-level privacy budget of 0.3, a vehicle-origin inference attack trained on independent simulations reaches 30% accuracy with correlated private quantization and 22% with independent quantization after 100 rounds, versus 91% without privacy and 10% for random guessing.

Tema degli esploratori: fiducia digitale e crittografia (elenco arXiv cs.CR, nuovi lavori del giorno).
Categorie arXiv: Machine Learning (cs.LG) ; Cryptography and Security (cs.CR).
Pagina del lavoro: https://arxiv.org/abs/2610.41055 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-03T05:23:42.991Z dalla pagina https://arxiv.org/list/cs.CR/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).