@Article{cmes.2026.081849,
AUTHOR = {Lilia Tightiz, L. Minh Dang, Hyosik Yang},
TITLE = {QFedFormer: A Privacy-Preserving Federated Transformer with Blockchain-Anchored Incentives for Dynamic EV Charging Pricing},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {},
YEAR = {},
NUMBER = {},
PAGES = {{pages}},
URL = {http://www.techscience.com/CMES/online/detail/27168},
ISSN = {1526-1506},
ABSTRACT = {We present QFedFormer, a federated transformer for dynamic electric vehicle (EV)-charging price prediction that combines quantization-aware training, SHAP-guided explainability, and blockchain-based incentives. The framework trains across distributed charging stations without centralizing user data, and programmable contracts set tariffs from forecasted demand and user-declared flexibility, while token rewards are derived from SHAP-based utility scores and anchored on-chain via Merkle proofs. On a real-world dataset, QFedFormer attains an energy-demand RMSE of 1.82±0.02 kWh and a tariff RMSE of 11.83±0.10 KRW/kWh (MAPE 2.7±0.2%) in the non-private baseline, outperforming FedAvg and Block-FeDL by 14.1% and 9.5%, respectively. Under client-level differential privacy (DP) with (σ=1.6,C=1,p=0.1,δDP=10−5), QFedFormer achieves (ε=2.0,δDP=10−5) after 50 rounds under a Rényi accountant, with forecast accuracy degrading modestly to 1.95 kWh RMSE (∼7.1% relative increase vs. non-private baseline). Blockchain evaluation shows an average audit latency of 58 ms per audit round, while a permissioned Ethereum-compatible deployment sustains more than 500 client updates per minute with gas costs of ∼$0.039/client per audit round. These results indicate that QFedFormer enables accurate, privacy-preserving, and auditable coordination of EV–grid interactions, offering both regulators and service providers a practical deployment pathway.},
DOI = {10.32604/cmes.2026.081849}
}