Open Access

ARTICLE

QFedFormer: A Privacy-Preserving Federated Transformer with Blockchain-Anchored Incentives for Dynamic EV Charging Pricing

Lilia Tightiz¹, L. Minh Dang^2,3, Hyosik Yang^1,*

1 Department of Computer Science and Engineering, Sejong University, 209, Neungdong-ro, Gwangjin-gu, Seoul, Republic of Korea
2 The Institute of Research and Development, Duy Tan University, Da Nang, Vietnam
3 Faculty of Information Technology, Duy Tan University, Da Nang, Vietnam

* Corresponding Author: Hyosik Yang. Email:

Computer Modeling in Engineering & Sciences 2026, 147(3), 46 https://doi.org/10.32604/cmes.2026.081849

Received 10 March 2026; Accepted 14 May 2026; Issue published 30 June 2026

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.

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