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arXiv Machine Learning By Hojat Allah Salehi, Devrim Aras, Md Jueal Mia, M. Hadi Amini, Imtiaz Karim, Mashrur Chowdhury, Bhavani Thuraisingham

Communication-Efficient Private Federated Reinforcement Learning with Post-Quantum Authentication for Vehicular Networks

Read the original on arXiv Machine Learning →

The paper proposes a communication‑efficient private federated reinforcement learning scheme for vehicular networks that uses correlated private quantization and post‑quantum authentication. By sharing complementary randomness between paired vehicles, the method reduces aggregation mean‑squared error while maintaining one‑bit communication and local differential privacy. Experiments show significant uplink savings, improved routing performance, and reduced privacy leakage compared to independent quantization.

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