Trustworthy Reputation for Federated Learning in O-RAN Using Blockchain and Smart Contracts

This paper proposes a blockchain-enabled framework to enhance trust, transparency, and collaboration in Open Radio Access Network (O-RAN) infrastructures through Federated Learning (FL). Traditional O-RAN architectures and centralized machine learning approaches face challenges when integrating multi-vendor environments, primarily due to lack of trust, proprietary data concerns, and limited interoperability. Our solution transitions from implicit trust, where the reliability of contributions is assumed, to explicit trust, where reputation is verifiably established on-chain. We introduce a blockchain-based reputation mechanism that evaluates the accuracy, integrity, and quality of participants' model updates within the FL process. Smart contracts automate critical tasks-such as participant registration, model update verification, and reputation scoring-ensuring that data inputs directly influence accountability in a tamper-proof, transparent manner. By deploying the framework on a scalable Layer 2 blockchain (Polygon) testnet and proposing the use of a blockchain oracle within this architectural framework for secure off-chain computations, this work focuses on a conceptual architectural approach by aligning with O-RAN's architecture to propose and deploy a Decentralized Application (DApp) on the blockchain. The proposed framework emphasizes a conceptual design over performance optimization and is structured to naturally benefit from ongoing improvements in blockchain scalability, which may reduce latency and enhance operational efficiency over time. Smart contracts for crucial processes and reputation calculation are included within our proposed DApp. The implementation of this work is publicly accessible https://github.com/farhanajaved/Reputation_O-RAN.