Open Access

ARTICLE

Blockchain-Assisted Improved Cryptographic Privacy-Preserving FL Model with Consensus Algorithm for ORAN

Raghavendra Kulkarni¹, Venkata Satya Suresh kumar Kondeti¹, Binu Sudhakaran Pillai², Surendran Rajendran^3,*

1 Department of Network Technology, T-Mobile USA Inc., Bellevue, WA 98006, USA
2 Department of Professional Services, Axyom.Core, North Andover, MA 01810, USA
3 Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, 602105, India

* Corresponding Author: Surendran Rajendran. Email:

Computers, Materials & Continua 2026, 86(1), 1-23. https://doi.org/10.32604/cmc.2025.069835

Received 01 July 2025; Accepted 16 September 2025; Issue published 10 November 2025

Abstract

The next-generation RAN, known as Open Radio Access Network (ORAN), allows for several advantages, including cost-effectiveness, network flexibility, and interoperability. Now ORAN applications, utilising machine learning (ML) and artificial intelligence (AI) techniques, have become standard practice. The need for Federated Learning (FL) for ML model training in ORAN environments is heightened by the modularised structure of the ORAN architecture and the shortcomings of conventional ML techniques. However, the traditional plaintext model update sharing of FL in multi-BS contexts is susceptible to privacy violations such as deep-leakage gradient assaults and inference. Therefore, this research presents a novel blockchain-assisted improved cryptographic privacy-preserving federated learning (BICPPFL) model, with the help of ORAN, to safely carry out federated learning and protect privacy. This model improves on the conventional masking technique for sharing model parameters by adding new characteristics. These features include the choice of distributed aggregators, validation for final model aggregation, and individual validation for BSs. To manage the security and privacy of FL processes, a combined homomorphic proxy-re-encryption (HPReE) and lattice-cryptographic method (HPReEL) has been used. The upgraded delegated proof of stake (Up-DPoS) consensus protocol, which will provide quick validation of model exchanges and protect against malicious attacks, is employed for effective consensus across blockchain nodes. Without sacrificing performance metrics, the BICPPFL model strengthens privacy and adds security layers while facilitating the transfer of sensitive data across several BSs. The framework is deployed on top of a Hyperledger Fabric blockchain to evaluate its effectiveness. The experimental findings prove the reliability and privacy-preserving capability of the BICPPFL model.

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Keywords

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